ABSTRACT:

In the current scenario, billions of objects are working as the part of IoT
and they are consistently sending, receiving,analysing data etc.IoT projects focus on
building smart cities by using the internet which makes life easier.IoT is used for
various applications such as healthcare systems,smart home applications etc. Security
issues are the major concern in any devices or in any application. This paper proposed
the study and analysis of IoT security issues. Cloud computing,encryption and
cryptography are the most common methods for IoT security and also discusses
recent security issues.

INTRODUCTION:
Internet of things(IoT) means connecting things to the internet and
controlling those things. It is the next vast evaluation in the wireless revolution.
Mattern and Floerkemeier (2010) defined the IoT as “controlled remotely and can act
as physical access points to the Internet services' '. Later,Huang, Craig, Lin, and Yan
(2016) defined it as “a worldwide network of physical objects using the Internet as a
communication media”.IoT has great application in real time, yet it has many
drawbacks that need to be resolved.
Alex koohang et al., discusses “This paper mainly speaks about the research models
with five constructs. IoT privacy and security knowledge tells that users have the
rights to their personal information as per their wish.IoT security knowledge and IoT
trust are the bridge between awareness and intention to use IoT"[1].
Debabrata Singh et al., describes "There are a lot of challenges in the implementation
of IoT in smart City applications. Due to the real world user interface, the IoT
ecosystem can't properly communicate between them. So there is a need for cloud
computing and IoT technologies"[2].In this paper, the author discussed cloud
computing and IoT for smart City applications.
Samundra Deep et al., describes "As the devices are increasingly just connected with
the network,there are privacy and security issues which make them more critical. By
using block chain technologies to rig the privacy and security issues to collect the data
in IoT. Due to poor standardisation in the IoT market every single connection makes
the network weak"[3].
Iqbal H. Sarker et al., describes "IoT is one of the most important technologies
anymore.,in terms of automation,protectivity and comfort for consumers across the
huge application areas, education to smart cities, Iot the promise for better quality of
human life. As an upshot they have briefly transverse how various types of machines
and deep learning approaches vigour employed for security solutions in IoT
context"[4].
Fadi Al-Turjman et al., describes "Attackers will have the ability to collect, transmit
and process the personal information of users like health, identity and location of the
user by accessing the surveillance system. This leads to the privacy leakage of the
user.Security and privacy requirements for smart cities services are privacy by design,
testing and verification, privacy architecture, data minimization, secret sharing,
system security and access control, and secure multiparty computation."[5]
Shashi Rekha et al., describes " Security is basically defined as the method that
assures protection. Until the client gives the important details and rejects the
unnecessary details, misuse of the information can be prevented[6]. "
Bara Nazzal et al., describes "This paper fully speaks about automated static analysis
and tools.They provide a higher level of flow and path sensitive analysis and It also
tells about the information leakage in smart things in IOT apps.The output of the
paper provides a quick and accurate analysis with high performance [7]."
Adil Adeel et al., describes “The IoT challenges are divided into two main
categories.They are technology challenge and security challenge. Technology
challenges include scalability,Standardisation,compatibility,and interoperability.
Security challenges include Data confidentiality,Data authenticity,Availability of
services,and Privacy of the entities[8].”
Edmond S. L. Ho describes " This paper we can see the improvement in the medicine
application in recent years. For automated diagnosis there are using Image-based Iot
systems. There are many types of Terminology training. More affordable and portable
cameras were used for better performance. Even though there are many advanced
techniques it also contains fewer risks. [9]"
"This survey explains the detection of APT and non APT attacks in IoT networks.
The methods that are used for detection are signature based, anomaly based and
hybrid based approaches. By the detection of an attack, IoT networks are
protected[10].
The security framework for IoT network.checksum,cyclic redundancy are the
methods used to check the originality of the data[11].
The IoT attacks are classified into four types. Identifying the attacks on the IoT
environment.Metasploits, aircrack-ng are the tools used to construct the experience
[12].
Stefan Balogh et al., describes "There are some Physical attacks,Network attacks,
Software attacks and Encryption attacks. Mainly they speak about the improvement of
security in the iot fieldfield and to secure their key factor by using cloud based
systems. The security of iot application is becoming a serious factor[13]."
In this literature paper,we discussed various security issues in IoT and we
came to know that by including a wide range of public services,we have many
advantages yet still we have some security threats. We should strengthen our
authentication system.(i.e,to avoid the security threats, we can find a different way of
authentication).
 Table:

S. AUTHOR & PUBLICATION & PROBLEM & METHODS OUTPUT

NO YEAR TITLE IDENTIFICATION

1. AUTHOR:Alex PUBLICATION: Attackers in smart Cloud computing A quick witted

Koohang , Carol Elsevier city based and IoT city.
Springer Sargent , based sources.
Jeretta Horn Nord , TITLE: Internet of
Joanna Things (IoT): From
Paliszkiewicz. awareness to
continued use
YEAR:2022

2. AUTHOR: PUBLICATION: Privacy,data,and Authentication, Good

Debabrata Singh, Springer identity theft. Encryption, understanding
Bibudhendu Pati, DOS,Device Access control, about IoT
Chhabi Rani TITLE:Security hijacking,PDoS, Security lifecycle security.
Panigrahi and Issues in IoT and Man in the middle. managements,
Shrabanee their Device
Swagatika Countermeasures in identification,
Smart City Security
YEAR:2022 Applications monitoring and
analysis,Security
lifecycle
management, and
application level
of DDOS
protection.

3. AUTHOR: PUBLICATION: DOS attack,Man-in- Authentication, The major

Samundra Deep ,Xi Wiley open access the-middle Encryption, security issues
Zhen, Alireza journal attack,malicious code Device and
Jolfaei,Dongjin injection attack, identification, Requirements for
Yu,Pouya Ostovari, TITLE:A survey of DDOS attack Security lifecycle security services
Ali Kashif Bashir. security and privacy management. are identified.
issues in the Internet
YEAR: 2022 of Things from the
layered context
4. AUTHOR:Iqbal H. PUBLICATION: Threats and weakness SVM, Layerwise cyber
Sarker, Asif Irshad Springer are occuring in IoT RF,NDC,LR, attacks help to
Khan, Yoosef B. devices. k-MEANS,FUZZ shield the IoT
Abushark and TITLE:Internet of Y system.
Fawaz Alsolami Things (IoT) Security CLUSTER,PCA,
Intelligence: A MLP,CNN,
YEAR:2022 Comprehensive REGRESSION
Overview, Machine REGULARISATI
Learning Solutions ON,FP-tree,
and Research MULTI-CNN,
Directions LSTM-RNN,
LSTM+CNN,LR

5. AUTHOR: PUBLICATION: Botnets destroy the Cryptography, Need effective

Fadi Al-Turjman Elsevier information in routers Biometrics, and efficient
Hadi Zahmatkesh etc. Machine learning solutions.
Ramiz Shahroze1 TITLE:An overview and data mining.
of security and
YEAR:2022 privacy in smart
cities' IoT
communications

6. AUTHOR: Shashi PUBLICATION: Security issues need Detection, Achieved

Rekha, Lingala Elsevier to be considered Encryption, software
Tirupati, Srikanth TITLE: Study of seriously as a whole Exposure, expansion.
Renikunta, Rekha security issues and system. Permission
Gangula solutions in internet managing,
of things Authentication.
YEAR: 2021

7. AUTHOR:BARA' PUBLICATION: Information leakage TXL,a function Quick and

NAZZAL , IEEE Access in smart things in IoT based hybrid accurate analysis
(Member, IEEE), apps. programming with high
AND MANAR H. TITLE:An language . performance.
ALALFI Automated Approach
for Privacy Leakage
YEAR: 2022 Identification in IoT
Apps

8. AUTHOR:Adil PUBLICATION: 1.Technological Symmetric and High level

Adeel Mazhar Ali Wiley open access challenges asymmetric security required.
Abdul Nasir Khan journal 2.Security cryptographic
Tauqeer Khalid challenges. techniques.
Faisal Rehman TITLE:A
Yaser Jararweh multi-attack resilient
Junaid Shuja lightweight IoT
authentication
YEAR: 2022 scheme
9. AUTHOR: PUBLICATION: Highly sensitive data Cloud computing, Based on the risk
Edmond S. L. Ho Springer is transferred in the BSN(body sensor of attack,provide
healthcare IoT network) a solution.
YEAR: 2022 TITLE:Data Security system. technology,
Challenges in Deep Hybrid encryption
Neural Network for system.
Healthcare IoT
Systems

10. AUTHOR: PUBLICATION: APT attacks and non Signature based, By deletion
ZHIYAN CHEN, ACM Computing APT attacks and their Anomaly based, methods,ATP
JINXIN LIU, surveys detection method. Hybrid attacks are
YU SHEN, approaches. avoided.
MURAT SIMSEK, TITLE:Machine
BURAK Learning-Enabled
KANTARCI, IoT Security: Open
HUSSEIN T. Issues and Challenges
MOUFTAH, Under Advanced
PETAR DJUKIC Persistent Threats

YEAR: 2022

11. AUTHOR:Abid PUBLICATION: SLR technique Checksum, Security

Ali, Abdul Mateen, researchgate.net proposed the security Cyclic framework
Abdul Hanan and framework. redundancy achieved by SLR
Farhan Amin TITLE:Advanced checks . techniques.
Security Framework
YEAR:2022 for Internet of Things
(IoT)

12. AUTHOR:Antti PUBLICATION: Eavesdropping Metasploit, The exact

Kuismanen Doria attacks,black hole Air crack-ng, solution is not
attack,grey hole Nethogs are tools. yet found.
YEAR:2022 TITLE:Security in attack,brute-force
Home IoT attacks,sybil attacks.
Environments

13. AUTHOR:Stefan PUBLICATION: Physical,Network, Identifying Overview of the

Balogh , Ondrej mdpi.com Software,and privacy, forward threats.
Gallo , Roderik Encryption attack. and backward
Ploszek , Peter TITLE:IoT Security security etc.
Špaˇcek and Pavol Challenges: Cloud
Zajac and Blockchain, Post
Quantum
YEAR:2021 Cryptography, and
Evolutionary
Techniques.
METHODS USED IN IoT SECURITY :

a) CLOUD COMPUTING:
The code cloud can be formed using automatic coding. Code clouds and
automatic coding are combined. This cloud is even more practical and is frequently
employed in information technology research to strengthen analysis. Software that can
identify the key themes and that is employed in studies presented in prior literature
can be used to conduct a quantitative analysis.
In addition to the Internet of Things, fog computing, and It continues to be
the second most prevalent motif in the papers examined. The third important theme,
data aggregation, was also mentioned and is still essential for the successful IoT.
Because of its development and advancement in information and cloud technologies,
cloud computing is widely used nowadays. It continues to be strong enough to offer
data security and reliable backups. So the data's accuracy can be successfully
obtained. The SLR was used to anticipate the most important themes covered in the
literature using a sample of articles, which were then analysed by software. An rising
interest in empirical research into the IoT and its determinants has been seen over the
last few decades. The SLR serves as the foundation for the data analysis's
significance.
The SLR of the chosen paper demonstrates that the data analysis layer places
a premium on data gathering in order to build and test intelligent conclusions.
Since dedicated sensors are not needed, IoT is becoming more and more popular. It is
feasible to offer customers continuous health monitoring in real life by combining
automated diagnosis and decision-making systems based on Deep Neural Networks
(DNN). The enormous computational cost of DNNs may make it difficult to create
systems with such potent capabilities.
Cloud computing may be a workable alternative, but when training data for
DNN models are uploaded, there may be data security concerns. DNN training often
involves large amounts of training data and expensive processing. The term "Machine
Learning as a Service" refers to the cloud-based deep learning solutions that cloud
service providers like Google, Amazon, and Microsoft are delivering (MLaas).
Uploading training data to remote servers run by outside organisations may cause data
security issues, even if these services give users flexibility over computational
resources. In order to provide incorrect predictions, attacks on the DNN model
training process are the main cause of data security problems.
 Fig.1. A general deep neural network training process

b) AUTHENTICATION:

Fig.2.Authentication

IoT devices should identify and authenticate one another.However,when so many

entities(device,people,etc.) are involved , authentication becomes a serious issue.
It gets complicated when the objects of IoT interact with each other for the first
time.So it requires a proper mechanism that authenticates entities in every interaction
to address this IoT security issue.

c) ENCRYPTION:
The process of converting information into a secret code that conceals
its true meaning is known as encryption. Many IoT devices employ symmetric
encryption, in which data is encrypted and decrypted using the same key. Data
encryption adds an extra degree of security, especially when compared to default
passcodes, but sharing and keeping the encryption key poses a serious security risk.
However, because asymmetric encryption uses two keys for encryption and
decryption, it can be a secure solution.

Fig.3.Asymmetric encryption Fig.4.Symmetric encryption

d) Signature based methods:

Malicious traffic patterns or application data are distinguished
from legitimate ones using signature detection systems. whether the network
behaviour and the malicious code's signature match. It is designated as abnormal.
Attacks can be recognised via signature detection, but only if a signature for the attack
is available. Due to the fact that this strategy simulates a novel type of attack, it will
perform badly.
In comparison to conventional network and security systems, the memory
constraints of IoT devices and their limited computational capacity pose a security
issue. Attack signature systems and packet payloads are compared while taking into
account the two detection methods' various processing costs. Consequently, a simple
security system that scans files for dangerous code using pattern matching. By
capping the simulated Internet of Things system's memory usage, the engine may
operate on hardware with restricted resources. With the intention of reducing
performance degradation due to the limitation on sensor level processing power and
available memory, they introduce two novel strategies named "auxiliary shifting" and
"early decision." These suggested solutions reduce the amount of matching operations
on resource-constrained systems, such as IoT networks, which improves efficiency
when resources are limited. Using a minimal digital signature, it is possible to provide
trustworthy communication between IoT networks for smart equipment with fewer
processes. The suggested approach protects against traffic analysis in IoT systems to
assure security. For low-power lossy networks using the 6LoWPAN protocol, a DoS
detection architecture is shown.
e) Anomaly-based Methods:

Fig.5.Anomaly-based method
An anomaly detection system compares a pattern found in a data stream to
baseline patterns of known hazards that have been previously stored. When the pattern
is consistent, action is performed in accordance with the current operating
requirements. The potential for the unavoidable false alarms is the drawback of
anomaly detection. The use of statistics, learning, and other techniques can be used to
categorise anomaly-based intrusion detection methods based on the applicable
methodology.
In a statistics-based method, network traffic activities are captured and used to
build a profile of the traffic's erratic behaviours. This profile takes into account a
number of crucial components. Two datasets are used in the network traffic anomaly
detection process. One contains the present observed profile, while the other is the
statistical profile that has already been trained. By contrasting the two actions as
network events take place and the determined profile, a quantitative value is obtained.
IDSs are able to identify irregularities using determined scores. On the one hand, the
strategies based on statistics result in better detection rates. The disadvantage of
statistical methods is that malevolent people could implant malicious code into the
systems and its parameters and get access.
The term "other detection techniques" refers to all methods that are not classified
under the statistical or machine learning groups. Examples of these methods include
control flow graphs, finite automata, and description languages. For instance, based on
the general-purpose Unified Modelling Language, this gives a framework to
determine incursion (UML). Software engineers can define intrusions using UML
notations that have been expanded to fit the context of different incursion scenarios
thanks to a UML profile called UMLintr. The framework does away with the need for
attack languages and makes use of UML's expressiveness. For the purpose of
describing attacks, learning UML is not necessary, which saves developers' time while
creating intrusion scenario specifications. For intrusion detection, it introduces a
description language. It demonstrates both anomaly detection and n-gram-based
categorization. Additionally, they create criteria for using data in various ways and
then apply the stated criteria to web intrusion detection. Due to time-consuming data
management brought on by a low-power limited memory environment, miscellaneous
detection techniques are ineffective for high quality and high volumes of data,
whereas miscellaneous including techniques are flexible and scalable. A variety of
works employ anomalous approaches to counter attacks on IoT systems, such as
wormhole, sinkhole, and DoS attacks. For instance, the study in studies a framework
for wormhole attack detection based on anomaly techniques, which is rarely explored
in current literature. a wormhole attack that is demonstrated at a 6LoWPAN adaptation
layer RPL network and is stopped by an IDS that is included into the Contiki OS.
In order to stop sinkhole attacks and guarantee security for IoT systems, the
author Yuxin Liu (2018) created the PRDSA scheme based on probe route. The
PRDSA architecture can be used to simultaneously detect, pass through, and locate
sinkhole attacks. Additionally, an IDDS system based on anomaly approaches is being
researched to counter DoS assaults in the IoT. DoS attacks, which are launched by
unscrupulous users in an effort to transmit a high volume of accurate data, make IoT
systems vulnerable. The authors suggest a system that does around 50% of the
computational work and offers dependable data distribution to fend off DoS assaults
in the Internet of Things.
Recently, ML algorithms have been available as tools to support anomaly-based
IDLs. ML methods are to implicit models that categorise data via pattern analysis. A
network intrusion system powered by machine learning can adjust its execution
strategy as it learns new facts. Systems for anomaly-based identification, such as
Bayesian, SVM, neural network, and outliers detection, use machine learning (ML)
methods. It demonstrates a thorough analysis presenting ML-based strategies aimed at
the Internet of Things for intelligent healthcare. The biggest drawback is the
requirement for relatively expensive computational resources in order to process the
required data.

f) Metasploit:
The Metasploit framework is an open source penetration testing tool. It
can be used to test different types of environments.Metasploit is used by many
companies and individuals to test the security of their environment and
product.Metasploit consists of a library of existing exploits to many operating
systems, specific IT tools and websites. The Metasploit framework is a good
beginning to learn about penetration testing. Metasploit allows the user to scan
specific operating systems and hosts for vulnerabilities. This scan is a good start to
find the entry points of a victim's home environment. Metasploit allows the user to
select an exploit to use and run it.
 Fig.5.Metasploit command tool.

g) Aircrack-ng:
Aircrack-ng is a suite of tools which can be used to test the security of the
wireless networks. Aircrack-ng allows the users to monitor and capture the wireless
packets that are stored in the files for further analysis. With the help of reply
attacks,fake access points and de authentication attacks,the suite can be used to attack
vulnerable wireless networks.

Fig.6.Aircrack-ng's command list.

h) TXL-HYBRID PROGRAMMING LANGUAGE:

A hybrid programming language called TXL[7] is employed to transform
programmes. The programming language TXL is based on functions and rules. The
grammar and transformation rules make up the two main components of this
programming language. Using an example, BARA' NAZZAL (Member, IEEE) et
al.,(2022) describe this programming language.
 Fig.8.Transformation rule

Fig.7.grammar part

Figure 7 illustrates the grammar portion of the programme that defines its syntax,
while Figure 8 illustrates the transformation rules that replace and change the inputted
code.[7]
In the above mentioned example, The code for a straightforward arithmetic
statement is in the grammar section. It defines the priorities, the structure, and the
syntax of arithmetic expressions. The transformation rule tries to add the two
matching numbers together by searching for the expression that matches the specified
pattern. The built-in expression in TXL expression for addition is the number [+
number].[7]

TAINT-THINGS:
TXL was used by BARA' NAZZAL (Member, IEEE) et al. for their statistical
analysis. They employed a tactic known as "TAINT ANALYSIS CORE MODULE."
Starting with the construction of a TXL grammar, sink statements are then marked,
analysed recursively, and so on until the fixed point is reached. Below is a diagram
that shows the taint things approach's architectural layout diagram(a).[7]
 Fig.9.Architecture of TXL

Next in precision enhancement, the authors of [7] used three algorithms.

Fig.10.Algorithm 1
 Fig.11.Algorithm 2

Fig.12.Algorithm 3

i) Hybrid Method:
A hybrid detection system combines anomaly- and signature-based approaches to
get the best of both worlds while overcoming their flaws. The advantages of the two
methods, such as the ability to detect "zero-day" attacks and the signature of known
attacks, can be combined by the hybrid detection system. It is challenging to develop
IoT networks in a straightforward manner because of the various communication
schemes, protocols, specific services, and technology in Traditional IDS.
However, a major problem with conventional IDS is its inability to adjust tactics
and services in real time. In order to accurately identify DoS attacks, zero-day attacks,
control hijacking attacks, and replay attacks in IoT contexts, the author developed a
hybrid IDS based on a timed automata controller technique. In order to find intrusions,
hybrid techniques like K-means clustering with Random Forest Classifiers and
Gaussian Mixture clustering with Random Forest Classifiers are used. Pradeep Singh
reports that the proposed hybrid technique reduced the false alert rate to 0.04c/o
(2018). Cristian Cervantes, author (2015) An IoT network's routing services can be
subject to sinkhole attacks, which is why the INTI hybrid IDS was created.In order to
monitor and audit packets sent between nodes and to extract reputation and trust
features for node evaluation,INTI combines anomaly-based concepts with
specification-based methodologies.

j) Collaborative Methods:
IoT applications are interested in collaborative IDSs. Compared to the other three
types of the three ways, it is different. Since collaborative approaches track and
compile aberrant acts, there is less demand on resources and less time spent
recognising suspicious activity. Collaboration between Internet of Things (IoT)
devices enables the gathering of data from various host and network devices to
produce promising detection in IoT networks; it enables effective detection with low
latency via collaboration among sensors and devices with limited resources in IoT
systems.Wenjuan Li's (2020) collaborative IDS, which incorporates a semi-supervised
ML model to handle the missing label problem, aims to increase detection accuracy.
Because labelling several instances in a network is expensive, it is possible to observe
the unlabeled network samples.Collaborative IDS is said to perform effectively with
unlabeled samples and have a decreased false alert rate.

Conclusion:
In this survey we can see the various growth of IoT in various fields. Even
though there is a lot of smart growth, it also contains some problematic holes in every
field. To solve these holes we should concentrate on some important areas that are,
● We have to make proper and correct access control. It helps to prevent hackers.
● The user's password should be changed often.
● The devices and the server must be updated often.
● Outdated software must be checked and it should be updated.
● We should strengthen the authentication by using Graphical Authentication.
● IoT is more sensitive because it contains a huge amount of user’s privacy data,
so we must strengthen privacy protection.
IoT is a comfort zone for users even though it is not applicable due to these reasons.
We should strengthen those areas by using graphical authentication, updated devices
and strong passwords.Huge number of different technologies have to be enforced to
support the deployment and further growth of IOT. Furthermore, there is a lot of scope
for IoT if those problems are solved.

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