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A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page1 / 22

RESEARCH(Survey Manuscript) Open Access

Human-centric Computing and Information Sciences (2021) 11:03

DOI: https://doi.org/10.22967/HCIS.2021.11.003
Received:September3, 2020;Accepted: December 21, 2020;Published: January 29,2021

A Comprehensive Survey on Core Technologies and

Services for 5G Security: Taxonomies, Issues, and
Solutions
Jin Ho Park1, Shailendra Rathore2, Sushil Kumar Singh2, Mikail Mohammed Salim2, Abir EL Azzaoui2,
Tae Woo Kim2, Yi Pan3, and Jong Hyuk Park2,*

Abstract
With the advancement of information and communication technologies, fifth-generation (5G) has become an
emerging communication medium to support higher speed, lower latency, and massive connectivity to
various devices by leveraging the evolution of 4G with the addition of new radio technology, service-based
architecture, and cloud infrastructure. Nonetheless, the introduction of new technologies and advanced
features in 5G communicationsgives rise to new security requirements and challenges. This paper presents a
comprehensive survey of various threats and solutions toward ensuring 5G security and privacy. The recent
development and existing schemes of 5G wireless security are offered based on the corresponding security
services, including authentication, availability, data confidentiality, integrity, and non-repudiation. We will
also discussthe different emerging technologies applied to 5G, such as Blockchain, software defined
networking, artificial intelligence, cyber-physical system, mobile edge computing, device-to-device (D2D)
communication, and Industry 4.0. Inspired by these security research and development activities in the
emerging technologies, we present various applications and services of 5Gconsidering the security
requirements and solutions. The challenges and future directions of 5G wireless security are finally
summarized.

Keywords
5G Security, Blockchain, Artificial Intelligence, Communication Technologies

1. Introduction

※This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
*Corresponding Author:Jong Hyuk Park(jhpark1@seoultech.ac.kr)
1
Dept. of Multimedia Engineering, Dongguk University, Seoul, Korea
2
Dept. of Computer Science and Engineering, Seoul National University of Science & Technology (SeoulTech), Seoul, Korea
3
Dept. of Computer Science, Georgia State University, Atlanta, GA, USA
page2 / 22

The fifth-generation (5G) wireless is the recent cellular technology to increase the speed and
responsiveness of 5G wireless networks for communication [1]. With the continuous advancement of
new technologies, 5G wireless networks are giving high data rates and low latency with maximum
coverage areas for improving communication globally. It will be deployed in stages over the next
several years to accommodate the increasing reliance on mobile and Internet-enabled devices. The 5G
will provide ultra-reliable, affordable broadband access at anyplace to a lot of smart devices for
Internet-oriented infrastructure such as cyber-physical system (CPS) [2] and Internet of Things (IoT)
[3,4].The 5G network is not a new network concept because it is the advancement of the 4G networks.
With the integration of new disruptive methodologies to meet the ever-growing demands of user traffic
and emerging services, IoT devices are known as 5G technology [5]. With all these requirements, 5G
provides various types of communication such as machine-to-machine (M2M), human-to-machine
(H2M), device-to-device (D2D), and others. Security is the main task for 5G wireless networks for
communication from one person to another.
When the concept of security is integrated with 5G technologies, then it has basic needs for 5G
networks and related protocols. The extension of 2G, 3G, and 4G networks is known as 5G networks.
The 2G, 3G, and 4G networks have various issues such as low latency and bandwidth, limited
resources, limited coverage areas, security, and privacy. Thus, 5G networks are utilized for
communication because it has new architecture and new services for security purposes [6, 7]. Still,
some security mechanism requires a little bit of modification for improving security for the
communication of networks. The OpenAirInterface (OAI) platform [8] is utilized in the latest
communication network such as 5G, providing improved security protocols and methods [9].
In the last few years, telecommunication has used various networks such as 2G, 3G, and 4G for
security and provided proper functionality for applications such as billing systems, and many schemes
were adopted. The encryption of communication data is used for security purposes for communication
[10]. The two-way authentication scheme is utilized in 3G networks to decrease the connection
generation with a base station [11, 12]. Advanced cryptographic protocols are being used in the 4G
network for user authentication. It provides security and privacy against physical attacks such as the
physical tampering of base stations.
The 5G security for communication,has three parts in the 5G networks: (1) all security threats and
requirements related to 2G, 3G, and 4G networks are applicable in 5G networks;(2) due to the
increasing number of IoT devices, users, network services, and requirements, 5G will also have some
open challenges such as security and privacy, network slices, security standardization, and device layer
security;(3) 5G introduces various network utilizations of new technologies such as software-defined
networks (SDN), network function virtualization (NFV), and network slices (NS) and poses new
challenges related to security and privacy [13].
This paper will discuss the core technologies and services for 5G security with various taxonomies. It
will describe multiple attacks generated in the 5G network and will also give solutions. The
convergence of new technologies such as Blockchain, SDN, artificial intelligence (AI), CPS, mobile
edge computing (MEC), D2D communication, Tactile Internet, and Industry 4.0 with 5G networks are
studied in this paper [14, 15]. Theapplication and services are described in relation to 5G networks.
Open research challenges are also discussed in the last part of this paper. The primary goal of our
survey is the study of core technologies and services for 5G security and integration of various smart
applications such as smart grid, smart drones, big data analysis, automotive driving, and IoT into 5G
security. The contribution of our study in relation to the existing survey is shown in Table 1.
The main contributions of our research are as follows:
• We study various technological aspects regarding 5G security.
• We describe in detailed tabular form the summary of various security threats in the 5G networks.
• We provide the existing solution for the security threats above in the 5G networks.
• We discuss the convergence of new technologies and paradigms such as Blockchain, SDN, AI,
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page3 / 22
CPS, MEC, D2D communication, Tactile Internet, and Industry 4.0 for 5G Security in the
networks.
• Finally, we will summarize and discuss the new applications and services and open research
challenges for 5G security.
Many researchers have studied and discussed the open research issues for 5G security with core
technologies and services. Kitanov et al. [16] mentioned an overview survey of 5G and fog computing
technologies. The impact of mobile devices using the 5G network gives rise to several key challenges.
With fog privacy and security threats, they also proposed security solutions using NFVas well as a
hybrid environment service orchestrator, along with resilient, reliable cloud computing for cloud
computing. Gandotraand Jha[17] presented an intensive survey on various energy-efficient scenarios
for green communication in 5G networks including D2D communication, spectrum sharing, ultra-dense
network (UDN), massive MIMO, millimeter-wave network, andIoT. A three-layer architecture was
proposed for improvingbattery life by using relays rather than the direct transmission of information, all
while providing the security umbrella for 5G networks by resisting intrusion as the density of base
stations and user equipment (UE) is being controlled and supervised constantly. Thus, an increase in
power consumption means that an intruder is authenticated to the network, where this approach makes
it easy to detect intruders and eliminate them from the 5G network. A survey on secure power
optimization has been deployed as well, and possible attacks within the small cell access points for the
5G scenario were also proposed. Ahmed et al. [18] presented an overview of the 5G security challenges
and solutions. Security threats impacting 5G networks are discussed along with specific problems that
affect mobile clouds, SDN and NFV, user privacy, and communication channels. The paper suggests
potential security solutions such as artificial intelligence and context awareness supporting SDN and
NFV to secure 5G network technologies. The survey notes that IoT gives rise to more security issues,
especially in terms of a user’s privacy in 5G networks. Zhang et al. [19] identified various security
issues from several perspectives, such as existing 4G networks, requirements from new architectures,
and challenges. They provide potential solutions for 5G security and privacy from various situations
with security architecture, cloud environment, new core networks, and radio technologies, which are all
discussed in detail. Several open challenges with future direction are also being debated on. Singh et al.
[20] proposed a machine learning-based network subslicing framework in a sustainable 5G
environment for the optimum performance of device application with the help of various network slice
resources in a sustainable 5G environment and addressed network load balancing issues. They used
four key considerations:latency, load balancing, heterogeneity, and power efficiency. Nonetheless they
did not provide services and applications for network subslicing in the 5G network.

Table 1.Contribution of our study in relation to the existing survey

Technological Services and
Research work Year Security issues Solutions
aspects applications
Kitanov al. [16] 2016 Yes No No Yes
Gandotraand Jha [17] 2017 No Yes Yes No
Ahmad et al. [18] 2018 No No No No
Zhang et al. [19] 2019 Yes No No Yes
Singh et al. [20] 2020 Yes Yes No No
Our study 2020 Yes Yes Yes Yes

The rest of the paper is organized as follows: in Section 2, we discuss the various existing survey on
core technologies and services for 5G security with related work; in Section 3, we study the
convergence of new technologies and paradigms for 5G security; in Section 4, we describe the
page4 / 22

applications and services related to 5G security. Finally, we conclude our paper in Section 5.

2. Related Work
In this section, we discuss the contribution of the existing surveys on core technologies and services
for 5G security. The 5G, also known as the fifth generation of mobile technologies, is expected to bring
about major changes in mobility and growth of IoT. Consisting of software-defined network and
network slices, 5G will allow dynamic programming to provide different applications for separate
layers over the network. According to the existing researchers who were mentioned, 5G realizes
latency, scalability, availability, reliability, ubiquitous mobility, and fog computing, which are needed
for critical massive IoT applications [21–24]. The categorization by 5G security is shown in Fig. 1 such
as availability, authentication, non-repudiation, integrity, and confidentiality.

Fig. 1.Categorized 5G security.

2.1 Technological Aspects

Availability: The 5G network-based radio access benefits from the resources from the cloud layer,
which helps in building a cost-effective infrastructure. Still, there are security concerns such as attacks
that disrupt the continued availability of network resources. Attacks such as Denial of Service disrupt
network slice operations and exhaust both logical resources at the fog layer and physical resources at
the cloud level. Jamming attacks degrade radio access resources, resulting in users being unable to
access cellular services [25]. Attacks on 5G resources such as control plane, support system, and radio
resources impact network are known as availability.
Authentication: Authentication is the fundamental concept of 5G security for verifying the identity of
users in the network. Several techniques are used for authenticating the information in the 5G network.
It has two parts: primary authentication and secondary authentication. Primary authentication provides
device and network mutual authentication in both 4G and 5G networks [26]. Nonetheless, primary
authentication in 5G has various challenges—such as control of knowledge—and call of device
authentication is not adequately provided. 5G-AKA and extensible authentication protocols are used for
mitigating these issues. Primary authentication works on non-3GPP technologies. Secondary
authentication is utilized for outside mobile operator domain, and it works on 3GPP. EAP-based
associated methods are applicable for secondary authentication.
Non-repudiation: Repudiation is the D2D communication that ensures that the users acknowledge the
transmission or reception of messages among themselves. Authentication by itself cannot prevent the
deniability of users. Nonetheless, authentication is essential to achieve non-repudiation as identification
of different users or UE is vital to establish secure data transmission. According to non-repudiation, it
ensures that the transferred data has been delivered and received by the parties claiming to have sent
and accepted the data in 5G networks.
Integrity: The 5G communication network has a security method between IoT devices and next-
generation node B (gNB) such as user plan integrity protection. It follows the property of encryption as
a feature in IoT devices and gNB. Integrity protection is the resource-demanding feature wherein all
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page5 / 22
IoT devices have constraints, so it cannot be supported at a maximum data rate. Thus, the 5G network
system provides some protocols for integrity protection.
Confidentiality: Confidentiality is a property of security. It means ensuring that the sender message is
only readable by the proposed destination in the 5G networks. The MeNB (master base node) derives
and sends the key to be used by the SgNB (secondary next-generation base node) prior to secured
communication over NR; the UE also derives the same key [27]. Unlike dual connectivity in 4G
networks, radio resource control (RRC) messages can be exchanged between the UE and SgNB; thus,
keys being used for integrity and confidentiality protection of RRC messages, including user plane
(UP) data, are all derived. Although integrity protection for UP data is supported in the 5G networks, it
will not be used in the EN-DC case. The use of confidentiality protection is optional for both UP and
RRC.

2.2 Security Issues

In this subsection, we will discuss the summary of various security threats in 5G networks as shown
in Table 2. It is categorized into various security threats with many fields such as Blockchain, SDN, AI,
CPS, MEC, D2D communication, Tactile Internet, and Industry 4.0.

Table 2.Summary of various security threats in 5G

Technological impact
Security attack Centralized Privacy Security
Server Block- Tactile Industry threata)
Wireless MEC SDN D2D CPS Cloud UDN
chain Internet 4.0
HX-DoS attack Cyber-physical No No No No No No No Yes Yes No No 1
cloud
SIPDAS attack Cyber-physical No No No No No No No Yes Yes No Yes 1
cloud
Byzantine generals Distributed No No No Yes No No No No No Yes Yes 4
attack computing
Jamming attacks Radio interface Yes Yes No No No No No No No No No 1
Spoofing attacks Physical layer Yes No No No No No No No No No Yes 2,3,4,5
Rogue base stations Centralized No Yes No No No No Yes No No No Yes 2,5
control element
MITM attack User equipment No Yes Yes No Yes No No No No No No 5
DoS attack Cloud No Yes Yes No Yes Yes No No No No No 1,4
Eavesdropping Fog computing No No No No Yes No No No No No No 4,5
Tamperingattack Fog and cloud No No No No Yes Yes No No No No No 4,5
computing
Smart attack Centralizedcontrol No Yes No No No No No No No No No 1,2,3,4,5
element
Privacy leakage User equipment No Yes No No No No No No No No No 5
Hijacking attack SDN controller No Yes Yes No No No No No No No No 1
Side-channel attack Network slices Yes No Yes No No No No No No No No 1,4,5
a)
The numbers indicate ―1‖ availability, ―2‖ authentication, ―3‖ non-repudiation, ―4‖ integrity, and ―5‖ confidentiality.

HX-DoS attack: This attack is the integration of HTTP and XML messages that are purposely sent
by the attacker to flood scripts and destroy the communication channel capacity of the cloud service
provider in the CPS infrastructure. With the help of web services such as infrastructure as a service
(IaaS), platform as a service (PaaS), and service as a service (SaaS), these attacks are easily deployed in
the cloud environment in CPS [28–30]. These kinds of attacks are easily resolved but will give rise to
various problems if they occur several times.
page6 / 22

SIPDAS attack: It is also related to the DoS attack, producing a legitimate SIP INVITE message in
the networks and transferring it to the receiver SIP component. Used via transmission control or user
datagram protocol, it has three different options for spoofed IP address generation, i.e., manual,
random, and by selecting the spoofed IP address from subnet. IP addresses could be specified manually
or generated randomly [31].
Byzantine general attack: In this attack, the adversary selectively drops the packets, modifies
certain packets, and forwards most of the data in its original form (encoded form) and, from time to
time, stops forwarding packets received by it but still actively participates in the network. It is like a
Byzantine general problem. In this problem, the commanding general must depend on the order to his
(n–1) lieutenant generals so that all loyal lieutenants obey the same order. If the commanding general is
loyal, then the loyal lieutenant obeys the order he sends [32].
Jamming attacks: The 5G network is susceptible to malicious attacks, which erode the performance
of the system. Wireless communication in 5G suffers from radio interface jamming attacks wherein the
control channels are essential for the radio interface to operate optimally. An attacker can hinder the
frequency bands by blocking selective control channels using high-powered, stealthy jamming attacks.
The severity of the jamming attacks increases if an attacker manages to compromise multiple devices
and forms a botnet. These compromised bot mobile devices can collectively function as jamming
devices [33, 34].
Spoofing attacks: An attacker can intercept legitimate conversations on a 5G network due to a
vulnerability known as spoofing. In spoofing attacks, an attacker injects fraudulent messages under a
false identity to receive illegal benefits and perform further malicious attacks such as denial of service
and man-in-the-middle attacks [35]. Spoofing attacks are one of the significant threats in 5G-based
wireless communications due to the vulnerability of physical layer attacks in wireless communications.
Rogue base station attacks: The introduction of automation in network optimization and
configuration for ideal network management introduced a new threat known as rogue base stations
(RBS) [36]. This attack is performed by an attacker pretending to be a legitimate base station to
conduct unauthorized and illegitimate surveillance for communication subversion and unwanted
advertising. By using false base stations, an attacker attempts to reveal the identity of subscribers by
storing the International Mobile Subscriber Identity of the user’s equipment.
MITM attack: In the man-in-the-middle (MITM) attack, a temporary scenario is created by an
attacker; this allows the interception of the data communication between the UEs over the network to
modify the content [37].
DoS attack: In this type of attack with denial of service (DoS), an attacker attempts to send fake data
to fog nodes, making the network unavailable for user authentication. Multiple network resources can
consume battery, time, bandwidth, etc. Several attacks in DoS can be compromised when a network is
used to carry out the attack.
Eavesdropping attacks: In this type of attack, when the transmission channel gets controlled by the
attacker, the attacker attempts to listen to or read the content over the network channel without the
user’s authorization and proposed various ideas to protect against the eavesdropping cyber-attack [38].
Tampering attacks: An attacker can cause delays or change the transmitted data over the network
channel without the user's authorization. The attacker can disrupt or erode the efficiency and
performance of fog computing. Such attacks can delay or cause the failure of data packets transmission
because of the wireless network and user’s mobility (UMs) but are hard to detect.
Smart attacks: A smart attacker can use smart radio devices to analyze the network status and
choose the right attack such as spoofing and jamming attacks depending on its distance from the target
edge node [39].
Privacy leak: Curious or illegal edge device owners may leak the information stored in their devices
and, in the worst-case scenario, sell them to a third party.
Hijacking attacks: It is used to consume the resources of the controller (i.e., data-to-control plane
saturation). An attacker’s goal is to slow down parts of the network or even make them unavailable by
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page7 / 22
consuming the controller's resources.
Side-channel attack: In 5G networks, the same physical infrastructure and resources are shared
between multiple slices, thereby facilitating a side-channel attack on the 5G network slices. Side-
channel attacks occur when a traducer succeeds in understanding certain physical patterns and
properties such as power consumption to retrieve sensitive information. Since 5G networks are based
on network slicing, an attacker can easily pick one slide and study its performance; thus, 5G networks
are more vulnerable to this kind of attack than previous network generations.

2.3 Existing Solutions

In this subsection, we provide the existing solutions in 5G networks for the aforesaid attacks, and
they are summarized in detailed tabular form in Table 3, which also has various categories such as
target component, technological impact, and privacy. The technological impact is also subcategorized,
such as Blockchain, AI, cryptography, cloud, CPS, MEC, and others.

Table 3.Summary of existing security solutions in 5G

Technological impact
Security solution Target component Privacy
MEC Blockchain AI SDN NFV CPS Cloud Cryptography
ENDER Centralized server No No No No No Yes Yes No Yes
SIPDAS attack simulator Physical cloud No No No No No Yes Yes No No
APG-BFT algorithm Centralized server No Yes No No No No No No Yes
Deep learning framework Wireless Android-based No No Yes No No No No No No
devices
Machine learning-based Physical layer authentication No No Yes No No No No No Yes
algorithm
Elliptic-ElGamal-based Secret key-based No No No No No No No Yes Yes
authentication scheme authentication
SDN-guard Centralized server No No No Yes Yes No Yes No Yes
Blood filter method Fog layer No No No Yes Yes No No No Yes
NFV method Edge computing No No No Yes Yes No Yes No Yes
SDN-5G Centralized server No No No Yes No No No Yes Yes
Deep Q-networklearning Edge server Yes No No No No No No No Yes
SDN-SC Core network No No Yes Yes No No No No No

ENDER: The full form of ENDER is a pre-dEcisioN, advaNce Decision, lEaRning system. As the
strategy for mitigating the HX-DoS and SIPDAS traffic attacks on the cloud environment in CPS, it has
two decision theory methods to detect attack traffic on the cloud, and then uses a similar technique as in
a traditional intrusion detection system. It can then identify and mark an attack message [40]. When
detecting the HX-DoS or SIPDAS attack message, then 1bit mark is added to the message. Reconstruct
and Drop RAD algorithms are used for removing such type of message in the system.
SIPDAS attack simulator: It is a strategy tool to simulate SIP-based DoS attacks, mitigating such
attacks from the cloud in CPS. This tool has mainly four components: IP address generator, SIP
message generator, message sender, and scenario player. It needs the output of SIP-NES (Network
scanner) and SIP-ENUM (Enumerator) along with some predefined files [41]. SIP-ENUM outputs
which SIP users are valid according to the responses in that network by sending register messages to
each client IP address on the production of SIP-NES. In this strategy, random ―INVITE‖ messages
containing no patterns within the messages are generated. Each generated ―INVITE‖ message is
grammatically compatible with SIP RFCs and acceptable to all the SIP components.
APG-BFT algorithm: It is a security authentication scheme based on Blockchain and used in 5G
UDN. This algorithm is mainly utilized for mitigating Byzantine general attacks in the network [42]. In
page8 / 22

this algorithm, trusted chain access point groups can be generated with APs, and authentication results
can be transferred in the APG through Blockchain message propagation. With this algorithm, we can
reduce the authentication frequency when user equipment moves various APs and improve access
efficiency.
Deep learning framework: Jamming attacks are mitigated when using AI-based deep learning
algorithms. Singh et al. [43] proposed a deep learning framework for smart city and to improve
jamming attack. The proposed deep learning algorithm learns the required features from the
heterogeneous wireless devices connected. Unsupervised learning is used to identify unknown attacks
in the network actively.
Machine learning-based algorithm: Spoofing attacks in 5G networks can be resolved when using
an authentication system in 5G networks using machine learning-based algorithms. Chen et al. [44]
proposed a physical layer authentication scheme to improve the authentication rate. Using a two-
dimensional feature in place of the one-dimensional method, the authentication model is strengthened
as it has stronger performance in detecting attackers. The proposed classifier can authenticate quickly
as the training of the classifier is done offline.
Elliptic-ElGamal-based authentication scheme: RBS-based attacks can be prevented by using
cryptography-based schemes. Abro et al. [45] proposed a lightweight elliptic-ElGamal-based
authentication scheme. The Elliptic curve cryptography is used to select a key pair, and the ElGamal
exchanges the secret key between the user equipment and the station. The main key consists of fewer
messages, which reduce the chances of guess-based attacks. The proposed scheme is beneficial for
devices with low computational power.
SDN-guard: SDN is considered a radical centralized network management structure that separates
the network; this enables innovations in networking programmability for emerging various applications
[46]. SDN-guard is proposed to address challenges based on security threats and provide solutions for
protecting MITM and DoS attacks in the Tactile Internet application with Fog systems [47].
Blood Filter Method: Li et al. [48] proposed the blood filter method for mitigating MITM attacks
with various security solutions. It has two parts—flooding controller and Open vSwitch (OVS)—to
monitor the system.
NFV Method: Distributed virtualization can highly increase the service, user, and network security;
deploying this technology can increase the availability and scalability of the networks and resolve
various attack challenges in different layers of the 5G network such as tapering, eavesdropping, and so
on [41, 49]. Porambage et al. [50] proposed third-party monitoring applications (PMAs) for detecting
anomalies to the network slices. During the transportation sending, encrypted data will prevent
tampering attacks.
SDN-5G: SDN-5G security architecture aims to tighten the security of 5G networks. The concept of
this approach uses a synchronized secret key generated when using an encryption algorithm and stored
in 5G devices and back end systems. During the communication phase, the attacker will need that
secret key in advance in order to alert the communication. In IP spoofing cases, the network will be
able to detect the message from attackers as the secret key changes over time; moreover, during an
MITM attack, the attacker hijacks a device’s communication in the authentication process. Still, the
back-end system will fail to update the secret key stored in the device; thus, unraveling the attack and
dropping it. As for the replay attack, following this architecture, the probability of this attack occurring
in the 5G network is close to zero as the secret key will change at each communication, making the
repeated message have an outdated key [51].
Deep Q-network learning: Artificial Intelligence can bring new security protocols to the network,
mainly Reinforcement Learning where an agent such as MEC edge node can observe the network
security complexity and learn from it using a Deep Q-network learning algorithm as proposed in [52].
SDN-SC: It is a software-defined security architecture for SDN-based 5G networks implemented in
order to solve the security problems in 5G core networks; accordingly, SDN’s logically centralized
intelligence, programmability, and abstraction have great advantages in solving mobile network
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page9 / 22
security problems. This proposed architecture can meet many of the security needs of users through its
programmability and deploy the service as soon as possible. Moreover, the architecture proves its
independence as the network management and security controller are working in parallel and they will
not affect each other or the normal work of the mobile network [53].

3. Convergence of New Technologies and Paradigms

In this section, we will describe various new technologies used in 5G. We primarily classified these
technologies into various categories. Therefore, we will be discussing the categories of technological
aspects and emerging technology in 5G security, which are shown in Fig. 2.

Fig. 2.Technological aspects, emerging technology in 5G security.

3.1 Blockchain for 5G Security

Blockchain refers to the disruptive and transformational technologies for 5G security to authenticate,
validate transactions, record information, and manage the identification among various parties in a
decentralized, secure manner [54, 55]. It is considered the big revolution for future communication
technologies in 5G. As a peer-to-peer, decentralized database platform for storing blocks of transaction
data linked together in chains, blockchain has various properties such as decentralizing, distributing,
and others in order to provide security in the 5G network. It is used in many applications such as smart
healthcare, smart banking, supply chain management, and driverless vehicles because it is a
decentralizing and distributing technology [56]. The combination of 5G and blockchain technology has
high potential to unleash a surge of economic value to share the data. The power of 5G coverage
through blockchain technology has reduced latency, high speeds, and capacity, enabling IoT devices to
be used widely. Simultaneously, these devices can leverage the security, decentralization, immutability,
and consensus arbitration of blockchain technology as a foundation layer. Blockchain can provide
consensus and protection while majority of IoT transactions and contracts occur on the network layer
with the opportunity to settle payment channels and transaction disputes on a chain. 5G will directly
assist blockchain technology by increasing node participation and decentralization as well as allowing
for shorter block times, driving forward on-chain scalability, and providing support to the IoT economy
in 5G.
Blockchain allows various parties to share, transfer, and access the data securely. A distributed ledger
page10 / 22

in blockchain has the required information that is transferred to all parties. Therefore, blockchain
technology provides more security features in the 5G network. In a transportation application,
blockchain offers a secure data accessibility mechanism wherein a passenger’s payment record data can
be accessed by various relevant bus transport parties in the system. Centralization and scalability are
also a significant issue in the IoT applications in the 5G network. In that case, blockchain-based
approaches offer decentralized security and privacy mechanism for various IoT applications in 5G. The
taxonomy for applications with 5G networks is shown in Fig. 3.
Blockchain provides a fast security authentication scheme for solving the APG trusted generation and
security, and it can efficiently access the user equipment in the UDN environment for 5G. According to
blockchain technology, various UDN systems generate consortium blockchain and user equipment
access to the APG, which is the combination of different APs. Local service center (LSC) manages the
APs clusters. That being said, UE performs secure and reliable access in the 5G environment with
Blockchain technology.

Fig. 3.Taxonomy for applications with 5G Networks.

3.2 CPS for 5G Security

The CPS is a very crucial smart system for 5G securities because it is the combination of the physical
and cyber worlds, interacting with the physical and computational components in 5G. CPS technology
is the security approach that ensures consistent protection of the entire cyber-physical ecosystem.
Sensors and actuators are often battery-powered, with modest resources that preclude implementing
computationally intensive security algorithms [57]. It interacts with the physical world as a process and
communicates information from one place to another between distributed elements in a cloud
environment with 5G [58]. It achieves the virtualization of network slices using cyber-physical clouds.
This cloud is using various types of sensors and actuators. These virtualized network components
provide cloud services in 5G. Existing research has many challenges, such as communication latency,
more resource requirement, accuracy, and others in 5G. To mitigate these challenges, we can use open-
source solution MANO that is utilized by many industrial organizations to promote the flexibility of 5G
for CPS. As a combination of hypervisor and container-based virtualization technologies, it has low
resource requirements, low latency, and flexibility in the 5G network. For achieving secure CPS
operations in 5G, it gives the security and industrial requirements in 5G such as lightweight and secure
processing (authentication, encryption, integrity).
Vulnerable CPS devices in a dynamic network topology require security visibility-related collection
of log data regarding 5G networks. CPS security in 5G needs not only confidentiality, integrity, and
availability; it must also ensure the veracity of sensors’ observations and maintain a plausible system
state at any time. HTTP and XML DoS (HX-DoS) attack is related to cloud-based CPS. In this attack,
HTTP and XML DoS messages are forwarded to the cloud by the attacker in the 5G environment [59].
To mitigate this attack, Ahmad et al.[41] proposed ENDER methods in a cloud-enabled CPS
environment. It has two decision theory methods to detect attack traffic on the cloud and uses a
technique similar to a traditional intrusion detection system. It is then able to identify and mark an
attack message. When detecting the HX-DoS or SIPDAS attack messages, the 1-bit mark is added to
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page11 / 22
the message. Reconstruct and Drop RAD algorithms are used for removing such type of messages in
the system.

3.3 Artificial Intelligence for 5G Security

AI is one of the most essential technologies for 5G security in order to manage a system that can
identify anomalies and predict future scenarios. Algorithms such as machine learning and deep learning
enable 5G networks to be both predictive and proactive in providing efficient and reliable services. AI-
based algorithms help realize the diverse requirements of the 5G technology to perform in an
automated, pre-programmed manner to fulfill the expected standards of the higher quality of experience
(QoE). The integration of AI for 5G security helps in predicting and forecasting degradation in the
performance of the network by monitoring traffic at the SDN-enabled switches. Various fraudulent
activities such as MITM attacks, radio jamming attacks, and other malicious activities can be traced and
identified by learning the historical flow pattern and analyzing the current traffic to prevent similar
attacks in the future. The summary of emerging technology for 5G securities is shown in Table 4.

Table 4.Summary of emerging technology for 5G security

Technology Description Technological aspects Impact Ref.
Blockchain It provides more securityfeatures in Decentralization, UDN Provide authenticated [54, 55, 69]
the 5G network with the communication between APs.
decentralized and distributed
method.
CPS It gives the security approach for Cloud-based CPS threats and Secure SaaS, PaaS,IaaS services to [67,68]
accessing secure cloud services solution such as ENDER the cloud.
onthe cloud network in 5G.
AI It enables 5Gnetworks to be both Using AI, 5G technology provides Provides security to 5G networks [44, 45,
predictive and proactive against higher Quality of Experience as it via secureauthentication and 60–62]
security attacks. performs in an automated manner. ensuringdataconfidentiality.
D2D It facilitates communication Devices exchange data with one D2D communicationimproves the [63–66]
communication between devices directly another without routing it toany 5G network
withouttherequirement of a Base 5G networkinfrastructure. performanceimproving spectral
station or cellular network. efficiency, network delay, and
cellular coverage.
Tactile Internet It will empower a lower latency for In order to reduce the latency use This technology in 5Gachieves [70–72]
the 5G network nonorthogonal multiple access high bandwidth, low latency end
communication system. (NOMA) based applications to end security, and availability.
specificlayerarchitecture and
networking slicing
Industry 4.0 It refers to the industrialrevolution Connects the cyber world with the Has the potential toexpand and [73]
technology, which focuses on the real world through CPS strengthen the competitiveness of
automation interconnectivity and companies in order toincrease the
real data time. flexibility, efficiency of
production.
MEC Provides computing, storage, and Allow applications to run closer to Empowering the network edge, [74]
networking resources within the the end-user, reduce latency, and optimizingmobile resources, pre-
edge of the mobile RAN facilitate the emergence of novel processing of large data and
applications such as Tactile providing context-awareservices.
Internet.
SDN Centralize the network control Facilitates quick threat Network securityprotocols can be [75–78]
platforms and enables identification, support highly deployedacross the network due to
programmability in reactive and proactive security global networkvisibility.
communication networks monitoring
Post-Quantum Secure 5G network against Secure key distributionsystem Prone against future Quantum [79–82]
cipher Quantum attacks and Quantum- between network users. attacks.
based cryptography.
page12 / 22

Authentication is an essential aspect of providing security to 5G connected devices. Wireless

communications and transmission protocols are open to interception and spoofing attacks.
Conventional authentication schemes have relied on cryptographic mechanisms, resulting in high
latency and computational overhead. Furthermore, these encryption mechanisms are not suitable for
computationally low-powered devices. AI-based intelligent authentication has the advantage of being
situationally aware, highly reliable, and cost-efficient. Chen et al. [44] proposed a physical-layer
channel authentication for 5G wireless communication using machine learning. Using a single- or
double-dimensional joint feature in the authentication method differentiated between a legitimate user
and a counterfeiter. The simulation-based results show that, compared with physical layer-based
authentication schemes, the proposed machine learning-based scheme has a higher rate in detecting
spoofing attacks. The authentication is done with data at a much faster rate as the classifier is trained
offline. Fang et al. [60] analyzed machine learning algorithms for authentication in 5G networks and
made a comparison between an intelligent authentication approach and a static authentication approach.
The static authentication scheme required more computational resources and had higher latency,
whereas the AI-based authentication scheme achieved fewer misdetection rates and had higher accuracy
in the authentication.
Data confidentiality is achieved by implementing access control to limit access to data to legitimate
users only and restricting access to unauthorized users. Shu et al. [61] proposed a machine learning-
based Direction on Arrival approach to secure confidential messages sent to users via the directional
modulation transmitter. The beamforming schemes and power allocations are reviewed; when using
machine learning, the eavesdropper is prevented from intercepting confidential messages intended for
the desired users. Mars et al. [62] proposed a privacy-preserving, reliable authentication novel
technique combining the user's biometric identity and a clone-resistant device identity. Machine
learning is used to build a user's biometric identification using keystroke dynamics and accelerometer.
The cloning-immune identity is both mobile operator- and manufacturer-independent. Combining both
identities establishes a secure authentication system and prevents the managing trusted authority from
intercepting the user's private links.

3.4 Quantum Cryptography for 5G Security

Quantum computers can solve complex mathematical problems exponentially faster than the current
computers. Nowadays, Quantum computers and Quantum-related information technology are being
developed at a fast rate, which threaten the classic public key cryptography used to secure 5G network
communication. To this end, securing the network against any possible Quantum attack is critical
before moving to the next network generation. To secure 5G network against Quantum attacks, using
post-Quantum cyphers is mandatory. Lattice-based cryptography is one of the possible and feasible
solutions. The first application of Lattice systems in cryptography was proposed by Ajtai [79], where
they used a random lattice picked based on a specific distribution explained in their proposal as a
random key. Lattice-based cryptography is used in numerous applications as it is theoretically proven to
be strong against Quantum attacks. One of the main lattice-based cryptosystem schemes is NTRU (Nth
degree truncated polynomial ring units), which is used for not only encryption but also signature
formulation [80]. The security of NTRU is based on solving the shortest vector problem and was
adopted in IEEE Standard 1363.1. As another feasible solution that is strong against Quantum attacks,
Quantum key distribution (QKD) is based on using individual photons to exchange cryptographic data
between users [81]. SK Telecom has been using QKD schemes since 2016 to secure LTE backhaul
network between Sejong and Daejeon in South Korea with over 350,000 subscribers [82]. In 2018, SK
Telecom applied the first Quantum cryptographic solution to 5G networks. Post-Quantum ciphers are
critical to 5G networks and beyond as the threat of Quantum attack will arise by the time.
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page13 / 22

4. Discussion and Future Scope

In this section, we will discuss the applications and services of 5G securities and also provide the
open research challenges for 5G securities in the network. The applications and services of 5G security
are shown in Fig. 4.

Fig. 4.Applications and services to 5G security.

4.1 Applications and Services to 5G Security

Healthcare: It is the most prominent application in the 5G network. Blockchain technology wields
high impact on the decentralized methodology of computation. In the healthcare domain, the secure
transfer of the patient’s health information is the main requirement to provide intelligent services
regarding 5G security because it is susceptible to data leak [83]. For this requirement, Blockchain
technology provides a secure data accessibility mechanism wherein patients’ health data can be easily
accessed by various doctors in the healthcare system [84]. Blockchain technology has also provided the
storage capacity for patient's and doctor's data in the healthcare system and used a centralized security
system [85, 86].
Big data analytics: 5G network in big data application plays an essential role within the network
computing and storage. While using the core features of 5G networks, radio access network, and core
network segments, big data can transport large volumes of structured and unstructured data to the data
centers effectively. Another benefit of big data in 5G is faster and real-time anomaly detection systems
from a large volume of data collected from connected devices. Parwez et al. [87] proposed a user
activity analysis and user anomaly detection system in 5G wireless networks. With the k-means and
hierarchical clustering along with big data call records, malicious activity in the network can be
identified.
page14 / 22

Internet of Things: IoT devices produce large volumes of data every day and require the efficient
transmission of data and large amounts of bandwidth, which 4G-based networks have struggled to
provide. 5G networks offer increased capacity in bandwidth, reduced latency, and improved data rate as
required by IoT devices. The next evolution in network technology has the technological ability to use
network slicing and fog computing to fulfill the requirements for future complex IoT architectures.
Security is still a significant concern for secured authentication and preservation of data confidentiality
for 5G data transmission with IoT devices. Ni et al. [88] proposed an efficient, reliable framework for
authentication and privacy. The slice selection mechanism is focused on maintaining the confidentiality
of users who are accessing services on network slices. The key-based agreement ensures secured
authentication and integrity of users. The framework retains data service confidentiality wherein users
can anonymously authenticate with IoT servers and securely access data cached on the fog layer.
Automotive driving: Automotive driving in the context of 5G network is being discussed as fully
automated vehicle steering and plotting as the new steps in terms of mobility. For the autonomous
reduction of traffic jams and accidents, sustainable and considerable reduction can be performed. The
time required to avoid a collision in the current safety application is below 10 milliseconds. Therefore,
latency is required in case of bidirectional data exchange for automatic driving, where it is technically
accomplished by Tactile Internet, at the same time providing high availability and reliability [89, 90].
Smart grid: It is part of Industrial 4.0, which uses the CPS in 5G technology that will allow
individualized solutions, flexibility, and cost-saving in the industrial process. To create effective
communication in the smart grid, security concerns are addressed before being deployed to any energy-
efficient system [91, 92]. Digital signature, timestamping, and Blockchain-based access control
technological methods are utilized for 5G security characteristics, such as non-repudiation and
integrity, and availability in smart grid applications.
Smart drones: 5G can efficiently accelerate the deployment of unmanned aerial vehicles (UAV)
base stations knows as drones base stations, especially with the usage of millimeter-wave and massive
number of connections. The previous radio frequency spectrum was below 6 GHz and was not capable
of supporting smart drones; with the usage of a large spectrum between 28 and 95 GHz, 5G can enable
effective communication between drones and ground users as it will enhance wireless mobile
broadband with low latency and high connection density. Moreover, the energy efficiency of 5G is a
complementary feature that can extend drones’ operation time [93]. Nonetheless, the adoption of smart
drones exposes the network to several security threats; specifically, drones could be used to launch
physical attacks on a smart city. In this case, network slicing and virtualization could be used to
mitigate the latter security challenge [94, 95]. Tag Key Encapsulation Mechanism (eCLSC-THEM),
and certificateless signcryption technological functions are used for 5G security characteristics such as
non-repudiation and integrity in smart drones application.

4.2 Open Research Challenges for 5G Security

This paper has presented various vulnerabilities and security measures to prevent and overcome
challenges in 5G networks. Technologies such as radio access and virtualization technologies such as
NFV and SDN are protected by using AI and Blockchain. Nonetheless, there are still some open
security issues in 5G systems such as data integrity in SDN devices, slice isolation in network slices,
and securing NFV interface tools. The vulnerabilities are as follows:
SDN security: AI-based algorithms will protect against abnormal network traffic and help optimize
the performance of the network. Nonetheless, several controllers and switches are deployed in the 5G
network, which requires security for the flow tables to ensure smoother operations in the system [96].
Blockchain technology provides data integrity in devices and ensures that other blocks verify any new
data added to the network. Data modified at SDN switches will require them to be verified by other
controllers as the planned flow rules are implemented at both controllers and switches [97, 98]. Another
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page15 / 22
critical issue in implementing AI-based algorithms for securing SDN is the availability of labeled data.
Data labeling is essential for AI based on the accurate detection of anomalous traffic in real time. High
costs and time limitations remain a challenge in implementing a real-time detection model using AI to
secure SDN.
Network slices: The 5G introduces a new multi-tenant-based network slice feature that improves
load balancing in the network. Security concerns such as DDoS based cyber-attacks that were discussed
in recent research address many overloading issues. Still, a single successful attack on a network slice
can be used to disable other network slices as these are separate logical networks. Furthermore, an
attack on a single slice may also be used to disable physical resources at the core network [99].
Network isolation is needed between network slices and physical resources. Access and security control
methods using machine learning and cryptographic-based solutions can protect other logical slices,
provide data confidentiality, and maintain scalability of the network [100–102]. Practical
implementation of SDN in network slicing remains a challenge in network slicing due to the lack of a
centralized security policy for different slices, which support varying services with separate security
and privacy policies. For example, a single slice’s security policy requiring additional computational
resources affects resource allocation for other network slices.
Network function virtualization: The NFV interface consists of many virtual tools such as
hypervisors, virtual machines, and network functions that need security. A compromise of any single
tool by attacks such as cyber and physical attacks hampers the performance of the system. Intrusion
detection systems and firewalls can protect the network from cyber-attacks, whereas trusted computing
can protect hypervisors from physical attacks [103]. Trusted computing ensures data privacy and
identification of malicious software in the virtual functions, assuring accountability that only trusted
software is operating on it. Hypervisor security is essential as virtual machines run on top of it;
disabling the hypervisor results in the degradation of the entire network's performance [104,105].
Security standardization: Many security standard groups are used in 5G securities, such as 3GPP,
5G PPP, ITU-T, NVF, ETSI, ANSI, and others, for resolving various security issues [106]. These
standard groups provide technical specifications, defining security protocols and M2M security
specifications and detecting and preventing security attacks along with others for 5G networks.
Nonetheless, these are not sufficient for the requirement to make large 5G networks efficient as well as
more precise 5G security mechanisms such as confidentiality, authentication, integrity, and non-
repudiation [107,108]. Lack of security standards affects the proper implementation of MEC and gives
rise to security issues such as user privacy protection and usage of devices. Thus, developing significant
standards for 5G network security is a requirement.
Device layer security: As a very crucial part of the 5G networks, it used various spread spectrum
techniques such as frequency hopping, sequence coding, encryption, and others for avoiding
eavesdropping [109]. Many researchers provide solutions for device layer security in the 5G networks,
but it is not sufficient because security in UAVs, millimeter-wave, D2D, and smart industries is not
received. Today, the integration of the device layer and Industry 4.0 is being utilized globally [110,
111]. Thus, there is a need to develop the device layer security in the 5G networks. Most network
devices in smart industries or base stations support the deployment of IoT devices for monitoring and
gathering data for improved services. These sensor nodes work in a D2D environment where mutual
sharing and data collaboration are essential for the optimized performance of 5G-supported real-time
smart city applications. A general lack of computational power in many IoT devices impedes the
implementation of effective security mechanisms.
Computation: With the recent advancement of technologies such as AI and neural networks, it does
not quickly resolve the security issues for new applications due to resource constraints and limited
computations [112, 113]. Thus, the 5G network needs new computation techniques for improving 5G
safety in the networks, and it will be utilizing new computation methods for overall 5G networks
because 5G networks are continuously used in various smart applications such as CPS, D2D, and
page16 / 22

Intelligent transportation system. A key area of challenge for CPS is the lack of sufficient concurrency
models in computing, which affects the 5G network’s ability to provide real-time performance.

5. Conclusion
With the recent development and existing schemes for 5G wireless networks, we discussed various
technological aspects and services for 5G security, such as availability, authentication, integrity, non-
repudiation, and confidentiality in this study. We presented a high-level taxonomy of different security
threats in 5G communication and provided existing solutions in detail in tabular form. We gave
possible general motivations behind the convergence of different emerging technologies applied to 5G
such as Blockchain, SDN, AI, CPS, MEC, D2D communication, Tactile Internet, and Industry 4.0 and
paradigms for 5G securities in the networks. Furthermore, we also discussed new applications such as
smart grid, smart drones, big data, automotive driving, smart healthcare, and IoT services for 5G
security. Finally, we described open research challenges for 5G security in the networks.
In the future, we will utilize core technologies including Blockchain, CPS, MEC, AI, D2D, Tactile
Internet, and Industry 4.0 and propose advanced architecture and framework for 5G security in smart
city applications such as smart manufacturing, smart transportation, and smart healthcare.

Author’s Contributions
Everyone in the author list has participated in the writing of this article, reviewed and revised the
article reasonably. All authors read and approved the final manuscript.

Funding
This study was supported by the Advanced Research Project funded by the SeoulTech (Seoul
National University of Science and Technology).

Competing Interests
The authors declare that they have no competing interests.

References
[1] G. Michaela, ―Introduction to 5G: your guide to fifth-generation wireless,‖ 2020 [Online]. Available:
https://searchnetworking.techtarget.com/definition/5G.
[2] H. Gong, R. Li, Y. Bai, J. An, and K. Li, ―Message response time analysis for automotive cyber–physical
systems with uncertain delay: anM/PH/1 queue approach,‖ Performance Evaluation, vol. 125, pp. 21-47,
2018.
[3] D. Kutscher, ―It's the network: towards better security and transport performance in 5G,‖ in Proceedings
of2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), San
Francisco, CA, 2016, pp. 656-661.
[4] N. Y. Kim, S. Rathore, J. H. Ryu, J. H. Park, and J. H. Park, ―A survey on cyber physical system security
for IoT: issues, challenges, threats, solutions,‖ Journal of Information Processing Systems, vol. 14, no. 6,
pp. 1361-1384, 2018.
[5] J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. K. Soong, and J. C. Zhang, ―What will
5G be?,‖ IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1065-1082, 2014.
[6] A. Gohil, H. Modi, and S. K. Patel, ―5G technology of mobile communication: a survey,‖ in Proceedings
of 2013 International Conference on Intelligent Systems and Signal Processing (ISSP), Gujarat, India,
2013, pp. 288-292.
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page17 / 22
[7] K. Gu, W. Jia, G. Wang, and S. Wen, ―Efficient and secure attribute-based signature for monotone
predicates,‖ Acta Informatica, vol. 54, no. 5, pp. 521-541, 2017.
[8] C. Sexton, N. J. Kaminski, J. M. Marquez-Barja, N. Marchetti, and L. A. DaSilva, ―5G: adaptable
networks enabled by versatile radio access technologies,‖ IEEE Communications Surveys & Tutorials, vol.
19, no. 2, pp. 688-720, 2017.
[9] P. Schneider and G. Horn, ―Towards 5G security,‖ in Proceedings of 2015 IEEE
Trustcom/BigDataSE/ISPA, Helsinki, Finland, 2015, pp. 1165-1170.
[10] J. Wang, C. Ju, Y. Gao, A. K. Sangaiah, and G. J. Kim, ―A PSO based energy efficient coverage control
algorithm for wireless sensor networks,‖ Computers, Materials & Continua, vol. 56, no.3, pp. 433-446,
2018.
[11] Z. Zhang, Y. Li, C. Wang, M. Wang, Y. Tu, and J. Wang, ―An ensemble learning method for wireless
multimedia device identification,‖ Security and Communication Networks, vol. 2018, article no.
5264526, 2018.
[12] M. Long, F. Peng, and H. Y. Li, ―Separable reversible data hiding and encryption for HEVC video,‖
Journal of Real-Time Image Processing, vol. 14, no. 1, pp. 171-182, 2018.
[13] P. K. Sharma, S. Rathore, and J. H. Park, ―Multilevel learning based modeling for link prediction and
users’ consumption preference in Online Social Networks,‖ Future Generation Computer Systems, vol.
93, pp. 952-961, 2019.
[14] B. Xiong, K. Yang, J. Zhao, W. Li, and K. Li, ―Performance evaluation of OpenFlow-based software-
defined networks based on queueing model,‖ Computer Networks, vol. 102, pp. 172-185, 2016.
[15] Y. Luo, K. Yang, Q. Tang, J. Zhang, P. Li, and S. Qiu, ―An optimal data service providing framework in
cloud radio access network,‖ EURASIP Journal on Wireless Communications and Networking, vol. 2016,
article no. 23, 2016.
[16] S. Kitanov, E. Monteiro, and T. Janevski, ―5G and the Fog — Survey of related technologies and
research directions,‖ in Proceedings of 2016 18th Mediterranean Electrotechnical Conference
(MELECON), Lemesos, Cyprus, 2016, pp. 1-6.
[17] P. Gandotra and R. K. Jha, ―A survey on green communication and security challenges in 5G wireless
communication networks,‖ Journal of Network and Computer Applications, vol. 96, pp. 39-61, 2017.
[18] I. Ahmad, T. Kumar, M. Liyanage, J. Okwuibe, M. Ylianttila, and A. Gurtov, ―Overview of 5G security
challenges and solutions,‖ IEEE Communications Standards Magazine, vol. 2, no. 1, pp. 36-43, 2018.
[19] S. Zhang, Y. Wang, and W. Zhou, ―Towards secure 5G networks: a survey,‖ Computer Networks, vol.
162, article no. 106871, 2019.
[20] S. K. Singh, M. M. Salim, J. Cha, Y. Pan, and J. H. Park, ―Machine learning-based network sub-slicing
framework in a sustainable 5G environment,‖ Sustainability, vol. 12, no. 15, article no. 6250, 2020.
[21] A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, M. S. Obaidat, and J. J. P. C. Rodrigues, ―Fog computing for
smart grid systems in the 5G environment: challenges and solutions,‖ IEEE Wireless Communications,
vol. 26, no. 3, pp. 47-53, 2019.
[22] J. C. S. Sicato, S. K. Singh, S. Rathore, and J. H. Park ―A comprehensive analyses of intrusion detection
system for IoT environment,‖ Journal of Information Processing Systems, vol. 16, no. 4, pp. 975-990,
2020.
[23] E. Yavuz, R. Yazici, M. C. Kasapbasi, and T. T. Bilgin, ―Improving initial flattening of convex-shaped
free-form mesh surface patches using a dynamic virtual boundary,‖ Computer Systems Science &
Engineering, vol. 34, no. 6, pp. 339-355, 2019.
[24] V. Pandey and P. Saini, ―Application layer scheduling in cloud: fundamentals, review and research
directions,‖ Computer Systems Science & Engineering, vol. 34, no. 6, pp. 357-376, 2019.
[25] C. Huang, J. Cao, and J. Cao, ―Stability analysis of switched cellular neural networks: a mode-dependent
average dwell time approach,‖ Neural Networks, vol. 82, pp. 84-99, 2016.
[26] F. Peng, R. S. Guo, C. T. Li, and M. Long, ―A semi-fragile watermarking algorithm for authenticating
2D CAD engineering graphics based on log-polar transformation,‖ Computer-Aided Design, vol. 42, no.
12, pp. 1207-1216, 2010.
[27] C. Huang and J. Cao, ―Almost sure exponential stability of stochastic cellular neural networks with
unbounded distributed delays,‖ Neurocomputing, vol. 72, no. 13-15, pp. 3352-3356, 2009.
page18 / 22

[28] A. Chonka and J. Abawajy, ―Detecting and mitigating HX-DoS attacks against cloud web services,‖ in
Proceedings of 2012 15th International Conference on Network-Based Information Systems, Melbourne,
VIC, Australia, 2012, pp. 429-434.
[29] A. A. M. Jamel and B. Akay, ―A survey and systematic categorization of parallel K-means and fuzzy-C-
means algorithms,‖ Computer Systems Science & Engineering, vol. 34, no. 5, pp. 259-281, 2019.
[30] S. K. Singh and S. Tanwar, ―Analysis of software testing techniques: theory to practical approach,‖
Indian Journal of Science and Technology, vol. 9, no. 32, pp. 1-6, 2016.
[31] M. Ficco and M. Rak, ―Stealthy denial of service strategy in cloud computing,‖ IEEE Transactions on
Cloud Computing, vol. 3, no. 1, pp. 80-94, 2015.
[32] ―SIP-Based DoS Attack Simulator: SIP-DAS,‖ 2017[Online]. Available: https://malware.news/t/sip-
based-dos-attack-simulator-sip-das/12413
[33] H. C. Chen and S. S. Kuo, ―Active detecting DDoS attack approach based on entropy measurement for
the next generation instant messaging App on smartphones,‖ Intelligent Automation and Soft Computing,
vol. 25, no. 1, pp. 217-228, 2019.
[34] C. R. Kumar and V. E. Jayanthi, ―A novel fuzzy rough sets theory based CF recommendation system,‖
Computer Systems Science & Engineering, vol. 34, no. 3, pp. 123-129, 2019.
[35] B. Xiong, K. Yang, J. Zhao, and K. Li, ―Robust dynamic network traffic partitioning against malicious
attacks,‖ Journal of Network and Computer Applications, vol. 87, pp. 20-31, 2017.
[36] K. Gu, Y. Wang, and S. Wen, ―Traceable threshold proxy signature,‖ Journal of Information Science
and Engineering, Vol. 33, no. 1, pp. 63-79, 2017.
[37] I. Mistry, S. Tanwar, S. Tyagi, and N. Kumar, ―Blockchain for 5G-enabled IoT for industrial automation:
a systematic review, solutions, and challenges,‖ Mechanical Systems and Signal Processing, vol. 135,
Article 106382, 2020.
[38] A. J. Akinyoade and O. T. Eluwole, ―The internet of things: definition, tactile-oriented vision, challenges
and future research directions,‖ in Third International Congress on Information and Communication
Technology. Singapore: Springer, 2018, pp. 639-653.
[39] L. Xiao, C. Xie, T. Chen, H. Dai, and H. V. Poor, ―A mobile offloading game against smart attacks,‖
IEEE Access, vol. 4, pp. 2281-2291, 2016.
[40] G. Konstantopoulos, ―Understanding blockchain fundamentals, part 1: byzantine fault tolerance,‖ 2017
[Online]. Available: https://medium.com/loom-network/understanding-blockchain-fundamentals-part-1-
byzantine-fault-tolerance-245f46fe8419.
[41] I. Ahmad, S. Shahabuddin, T. Kumar, J. Okwuibe, A. Gurtov, and M. Ylianttila, ―Security for 5G and
beyond,‖ IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3682-3722, 2019.
[42] Cloud Security Alliance, ―Top threats to cloud computing V1.0,‖ 2010 [Online]. Available:
https://ioactive.com/wp-content/uploads/2018/05/csathreats.v1.0-1.pdf.
[43] S. K. Singh, Y. S. Jeong, and J. H. Park, ―A deep learning-based IoT-oriented infrastructure for secure
smart City,‖ Sustainable Cities and Society, vol. 60, article no. 102252, 2020.
[44] S. Chen, H. Wen, J. Wu, J. Chen, W. Liu, L. Hu, and Y. Chen, ―Physical-layer channel authentication for
5G via machine learning algorithm,‖ Wireless Communications and Mobile Computing, vol. 2018,
article no. 6039878, 2018.
[45] A. Abro, Z. Deng, and K. A. Memon, ―A Lightweight elliptic-ElGamal-based authentication scheme for
secure device-to-device communication,‖ Future Internet, vol. 11, no. 5, article no. 108, 2019.
[46] X. Duan and X. Wang, ―Authentication handover and privacy protection in 5G HetNets using software-
defined networking,‖ IEEE Communications Magazine, vol. 53, no. 4, pp. 28-35, 2015.
[47] L. Dridi and M. F. Zhani, ―SDN-Guard: DoS attacks mitigation in SDN networks,‖ in Proceedings of
2016 5th IEEE International Conference on Cloud Networking (Cloudnet), Pisa, Italy, 2016, pp. 212-
217.
[48] C. Li, Z. Qin, E. Novak, and Q. Li, ―Securing SDN infrastructure of IoT–Fog networks from MitM
attacks,‖ IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1156-1164, 2017.

[49] A. E. Azzaoui, S. K. Singh, Y. Pan, and J. H. Park, ―Block5GIntell: blockchain for AI-enabled 5G
networks,‖ IEEE Access, vol. 8, pp. 145918-145935, 2020.
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page19 / 22
[50] P. Porambage, Y. Miche, A. Kalliola, M. Liyanage, and M. Ylianttila, ―Secure keying scheme for
network slicing in 5G architecture,‖ in Proceedings of 2019 IEEE Conference on Standards for
Communications and Networking (CSCN), GRANADA, Spain, 2019, pp. 1-6.
[51] J. Yao, Z. Han, M. Sohail, and L. Wang, ―A robust security architecture for SDN-based 5G networks,‖
Future Internet, vol. 11, no. 4, article no. 85, 2019.
[52] L. Xiao, X. Wan, C. Dai, X. Du, X. Chen, and M. Guizani, ―Security in mobile edge caching with
reinforcement learning,‖ IEEE Wireless Communications, vol. 25, no. 3, pp. 116-122, 2018.
[53] X. Liang and X. Qiu, ―A software defined security architecture for SDN-based 5G network,‖ in
Proceedings of 2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-
NIDC), Beijing, China, 2016, pp. 17-21.
[54] Z. Chen, S. Chen, H. Xu, and B. Hu, ―A security authentication scheme of 5G ultra-dense network based
on block chain,‖ IEEE Access, vol. 6, pp. 55372-55379, 2018.
[55] J. Cha, S. K. Singh, Y. Pan, and J. H. Park, ―Blockchain-based cyber threat intelligence system
architecture for sustainable computing,‖ Sustainability, vol. 12, no. 16, article no. 6401, 2020
[56] J. Park, M. M. Salim, J. H. Jo, J. C. S. Sicato, S. Rathore, and J. H. Park, ―CIoT-Net: a scalable cognitive
IoT based smart city network architecture,‖ Human-centric Computing and Information Sciences, vol. 9,
article no. 29, 2019. https://doi.org/10.1186/s13673-019-0190-9
[57] A. Chaer, K. Salah, C. Lima, P. P. Ray, and T. Sheltami, ―Blockchain for 5G: opportunities and
challenges,‖ in Proceedings of 2019 IEEE Globecom Workshops, Waikoloa, HI, 2019, pp. 1-6.
[58] J. Kim, G. Jo, and J. Jeong, ―A novel CPPS architecture integrated with centralized OPC UA server for
5G-based smart manufacturing‖ Procedia Computer Science, vol. 155, pp. 113-120, 2019.
[59] S. Zhang, Y. Lin, Q. Liu, J. Jiang, B. Yin, and K. K. R. Choo, ―Secure hitch in location based social
networks,‖ Computer Communications, vol. 100, pp. 65-77, 2017.
[60] H. Fang, X. Wang, and S. Tomasin, ―Machine learning for intelligent authentication in 5G and beyond
wireless networks,‖ IEEE Wireless Communications, vol. 26, no. 5, pp. 55-61, 2019.
[61] F. Shu, Y. Qin, R. Chen, L. Xu, T. Shen, S. Wan, S. Jin, J. Wang, and X. You, ―Directional modulation:
a secure solution to 5G and beyond mobile networks,‖ 2019 [Online]. Available
https://arxiv.org/abs/1803.09938.
[62] A. Mars, A. Abadleh, and W. Adi, ―Operator and manufacturer independent D2D private link for future
5G networks,‖ 2019 [Online]. Available: https://arxiv.org/abs/1911.00303.
[63] R. I. Ansari, C. Chrysostomou, S. A. Hassan, M. Guizani, S. Mumtaz, J. Rodriguez, and J. J. P. C.
Rodrigues, ―5G D2D networks: techniques, challenges, and future prospects,‖ IEEE Systems Journal,
vol. 12, no. 4, pp. 3970-3984, 2018.
[64] M. Wang and Z. Yan, ―Privacy-preserving authentication and key agreement protocols for D2D group
communications,‖ IEEE Transactions on Industrial Informatics, vol. 14, no. 8, pp. 3637-3647, 2018.
[65] L. Jiang, S. Xie, S. Maharjan, and Y. Zhang, ―Joint transaction relaying and block verification
optimization for blockchain empowered D2D communication,‖ IEEE Transactions on Vehicular
Technology, vol. 69, no. 1, pp. 828-841, 2020.
[66] S. Pizzi, C. Suraci, L. Militano, A. Orsino, A. Molinaro, A. Iera, and G. Araniti, ―Enabling trustworthy
multicast wireless services through D2D communications in 5G networks,‖ Future Internet, vol. 10, no.
7, article no. 66, 2018.
[67] S. Mumtaz, A. Bo, A. Al-Dulaimi, and K. F. Tsang, ―Guest editorial 5G tactile internet: an application
for industrial automation,‖ IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 2992-2994,
2019.
[68] A. A. Ateya, A. Muthanna, A. Vybornova, I. Gudkova, Y. Gaidamaka, A. Abuarqoub, A. D. Algarni,
and A. Koucheryavy, ―Model mediation to overcome light limitations—toward a secure tactile internet
system,‖ Journal of Sensor and Actuator Networks, vol. 8, no. 1, article no. 6, 2019.
[69] N. Panwar, S. Sharma, and A. K. Singh, ―A survey on 5G: the next generation of mobile communication,‖
Physical Communication, vol. 18(pt. 2), pp. 64-84, 2016.
[70] K. Zhou, T. Liu, L. Zhou, ―Industry 4.0: towards future industrial opportunities and challenges,‖ in
Proceedings of2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD),
Zhangjiajie, China, 2015, pp. 2147-2152.
page20 / 22

[71] I. Budhiraja, S. Tyagi, S. Tanwar, N. Kumar, and J. J. P. C. Rodrigues, ―Tactile internet for smart
communities in 5G: an insight for NOMA-based solutions,‖ IEEE Transactions on Industrial
Informatics, vol. 15, no. 5, pp. 3104-3112, 2019.
[72] Y. Yu, H. Chen, Y. Li, Z. Ding, and B. Vucetic, ―On the performance of non-orthogonal multiple access
in short-packet communications,‖ IEEE Communications Letters, vol. 22, no. 3, pp. 590-593, 2018.
[73] A. Varghese and D. Tandur, ―Wireless requirements and challenges in Industry 4.0,‖ in Proceedings of
2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India,
2014, pp. 634-638.
[74] T. X. Tran, A. Hajisami, P. Pandey, and D. Pompili, ―Collaborative mobile edge computing in 5G
networks: new paradigms, scenarios, and challenges,‖ IEEE Communications Magazine, vol. 55, no. 4,
pp. 54-61, 2017.
[75] I. Ahmad, T. Kumar, M. Liyanage, J. Okwuibe, M. Ylianttila, and A. Gurtov, ―5G security: analysis of
threats and solutions,‖ in Proceedings of 2017 IEEE Conference on Standards for Communications and
Networking (CSCN), Helsinki, Finland, 2017, pp. 193-199.
[76] S. Sezer, S. Scott-Hayward, P. K. Chouhan, B. Fraser, D. Lake, J. Finnegan, N. Viljoen, M. Miller, and
N. Rao, ―Are we ready for SDN? Implementation challenges for software-defined networks,‖ IEEE
Communications Magazine, vol. 51, no. 7, pp. 36-43, 2013.
[77] V. Vassilakis, I. P. Chochliouros, A. S. Spiliopoulou, E. Sfakianakis, M. Belesioti, N. Bompetsis, M.
Wilson, C. Turyagyenda, and A. Dardamanis, ―Security analysis of mobile edge computing in
virtualized small cell networks,‖ in Artificial Intelligence Applications and Innovations. Cham,
Switzerland: Springer, 2016, pp. 653-665.
[78] N. Abbas, Y. Zhang, A. Taherkordi, and T. Skeie, ―Mobile edge computing: a survey,‖ IEEE Internet of
Things Journal, vol. 5, no. 1, pp. 450-465, 2018.
[79] M. Ajtai, ―Representing hard lattices with O(n log n) bits,‖ in Proceedings of the Thirty-Seventh Annual
ACM Symposium on Theory of Computing, Baltimore, MD, USA, 2005, pp. 94-103.
[80] T. C. Clancy, R. W. McGwier, and L. Chen, ―Post-quantum cryptography and 5G security: tutorial,‖ in
Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks, Miami,
FL, 2019, p. 285.
[81] A. E. Azzaoui and J. H. Park, ―Post-quantum blockchain for a scalable smart city,‖ Journal of Internet
Technology, vol. 21, no. 4, pp. 1171-1178, 2020.
[82] D. H. Sim, ―Quantum safe communication – cybersecurity for 5G era,‖ in Proceedings of the 4th ITU
Workshop on Future Network 2030, St. Petersburg, Russia, 2019.
[83] S. K. Singh, M. M. Salim, M. Cho, J. Cha, Y. Pan, and J. H. Park, ―Smart contract-based pool hopping
attack prevention for blockchain networks,‖ Symmetry, vol. 11, no. 7, article no. 941, 2019.
[84] V. Ramani, T. Kumar, A. Bracken, M. Liyanage, and M. Ylianttila, ―Secure and efficient data
accessibility in blockchain based healthcare systems,‖ in Proceedings of 2018 IEEE Global
Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp. 206-212.
[85] S. K. Singh, S. Rathore, and J. H. Park, ―BlockIoTIntelligence: a blockchain-enabled Intelligent IoT
architecture with artificial intelligence,‖ Future Generation Computer Systems, vol. 110, pp. 721-743,
2020.
[86] X. Yue, H. Wang, D. Jin, M. Li, and W. Jiang, ―Healthcare Data Gateways: found healthcare intelligence
on blockchain with novel privacy risk control,‖ Journal of Medical Systems, vol. 40, no. 10, article no.
218, 2016.
[87] M. S. Parwez, D. B. Rawat, and M. Garuba, ―Big data analytics for user-activity analysis and user-
anomaly detection in mobile wireless network,‖ IEEE Transactions on Industrial Informatics, vol. 13,
no. 4, pp. 2058-2065, 2017.
[88] J. Ni, X. Lin, and X. S. Shen, ―Efficient and secure service-oriented authentication supporting network
slicing for 5G-enabled IoT,‖ IEEE Journal on Selected Areas in Communications, vol. 36, no. 3, pp.
644-657, 2018.
[89] I. Da Silva, G. Mildh, J. Rune, P. Wallentin, J. Vikberg, P. Schliwa-Bertling, and R. Fan, ―Tight
integration of new 5G air interface and LTE to fulfill 5G requirements,‖ in Proceedings of 2015 IEEE
81st Vehicular Technology Conference (VTC Spring), Glasgow, UK, 2015, pp. 1-5.
A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions page21 / 22
[90] M. Simsek, A. Aijaz, M. Dohler, J. Sachs, and G. Fettweis, ―5G-enabled tactile internet,‖ IEEE Journal
on Selected Areas in Communications, vol. 34, no. 3, pp. 460-473, 2016.
[91] S. Reka, T. Dragicevic, P. Siano, and S. R. S. Prabaharan, ―Future generation 5G wireless networks for
smart grid: a comprehensive review,‖ Energies, vol. 12, no. 11, article no. 2140, 2019.
[92] Y. S. Jeong and J. H. Park, ―Security, privacy, and efficiency of sustainable computing for future smart
cities,‖ Journal of Information Processing Systems, vol. 16, no. 1, pp. 1-5, 2020.
[93] D. Loghin, S. Cai, G. Chen, T. T. A. Dinh, F. Fan, Q. Lin, et al., ―The disruptions of 5G on data-driven
technologies and applications,‖ 2019 [Online]. Available: https://arxiv.org/abs/1909.08096.
[94] E. Vattapparamban, I. Guvenç, A. I. Yurekli, K. Akkaya, and S. Uluagaç, ―Drones for smart cities:
issues in cybersecurity, privacy, and public safety,‖ in Proceedings of 2016 International Wireless
Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus, 2016, pp. 216-221.
[95] C. Yin, S. Ding, and J. Wang, ―Mobile marketing recommendation method based on user location
feedback,‖ Human-centric Computing and Information Sciences, vol. 9, article no. 14, 2019.
https://doi.org/10.1186/s13673-019-0177-6
[96] P. K. Sharma, S. Rathore, Y. Jeong, and J. H. Park, ―SoftEdgeNet: SDN based energy-efficient
distributed network architecture for edge computing,‖ IEEE Communications Magazine, vol. 56, no. 12,
pp. 104-111, 2018.
[97] Z. Deng, Y. Ren, Y. Liu, X. Yin, Z. Shen, and J. H. Kim, ―Blockchain-based trusted electronic records
preservation in cloud storage,‖ Computers, Materials & Continua, vol. 58, no.1, pp. 135-151, 2019.
[98] J. Wang, Y. Gao, W. Liu, W. Wu, and S. J. Lim, ―An asynchronous clustering and mobile data gathering
schema based on timer mechanism in wireless sensor networks,‖ Computers, Materials & Continua, vol.
58, no. 3, pp. 711-725, 2019.
[99] L. H. Juang and S. Zhang, ―Intelligent service robot vision control using embedded system,‖ Intelligent
Automation & Soft Computing, vol. 25, no.3, pp. 451-458, 2019.
[100] Y. Li, H. Gao, and Y. Xu, ―Special section on big data and service computing,‖ Intelligent Automation
& Soft Computing, Vol. 25, no. 3, pp. 511-512, 2019.
[101] C. Li, X. Shang, L. Zhang, F. Yang, J. Zheng, and X. Xu, ―Pipeline defect detection cloud system using
role encryption and hybrid information,‖ Computers, Materials & Continua, vol. 61, no.3, pp. 1245-
1260, 2019.
[102] D. Xiao, J. Liang, Q. Ma, Y. Xiang, and Y. Zhang, ―High capacity data hiding in encrypted image
based on compressive sensing for nonequivalent resources,‖ Computers, Materials & Continua, vol. 58,
no.1, pp. 1-13, 2019.
[103] J. S. Sheu, I. C. Chen, and Y. S. Liao, ―Realization of internet of things smart appliances,‖ Intelligent
Automation & Soft Computing, vol. 25, no. 2, pp. 395-404, 2019.
[104] M. Okhovvat and M. R. Kangavari, ―TSLBS: a time-sensitive and load balanced scheduling approach
to wireless sensor actor networks,‖ Computer Systems Science & Engineering,vol. 34, no.1, pp. 13-21,
2019.
[105] M. Liu, L. Cheng, K. Qian, J. Wang, J. Wang, and Y. Liu, ―Indoor acoustic localization: a survey,‖
Human-centric Computing and Information Sciences, vol. 10, article no. 2, 2020.
https://doi.org/10.1186/s13673-019-0207-4
[106] D. Gao, S. Zhang, F. Zhang, X. Fan, and J. Zhang, ―Maximum data generation rate routing protocol
based on data flow controlling technology for rechargeable wireless sensor networks,‖ Computers,
Materials & Continua, vol. 59, no.2, pp. 649-667, 2019.
[107] C. Yin, B. Zhou, Z. Yin, and J. Wang, ―Local privacy protection classification based on human-centric
computing,‖ Human-centric Computing and Information Sciences, vol. 9, article no. 33, 2019.
https://doi.org/10.1186/s13673-019-0195-4
[108] Y. Xue, Q. Li, T. Wu, L. Feng, L. Zhao, and F. Yu, ―Incorporating stress status in suicide detection
through microblog,‖ Computer Systems Science & Engineering, vol. 34, no.2, pp. 65-78, 2019.
[109] S. Zhong, ―Heterogeneous memristive models design and its application in information security,‖
Computers, Materials & Continua, vol. 60, no.2, pp. 465-479, 2019.
page22 / 22

[110] J. Guan, J. Li and Z. Jiang, ―The design and implementation of a multidimensional and hierarchical
web anomaly detection system,‖ Intelligent Automation & Soft Computing, vol. 25, no.1, pp. 131-141,
2019.
[111] T. M. Li, H. C. Chao, and J. Zhang, ―Emotion classification based on brain wave: a survey,‖ Human-
centric Computing and Information Sciences, vol. 9, article no. 42, 2019.
https://doi.org/10.1186/s13673-019-0201-x
[112] S. Muhammad, T. A. Malik, R. Khatoon, S. S. Hassan, and F. A. Shah, ―Human behavior classification
using geometrical features of skeleton and support vector machines,‖ Computers, Materials &
Continua, vol. 61, no.2, pp. 535-553, 2019.
[113] C. W. Hung, W. L. Mao, and H. Y. Huang, ―Modified PSO algorithm on recurrent fuzzy neural
network for system identification,‖ Intelligent Automation & Soft Computing, vol. 25, no.2, pp. 329-
341, 2019.