An Industry-Oriented Mini Project Report

On
The Influence of Artificial Intelligence on E-Governance and
Cybersecurity in Smart Cities: A Stakeholder’s Perspective
Submitted in Partial Fulfillment of the
Academic Requirement for the Award of
Degree
BACHELOR OF TECHNOLOGY

in
Computer Science and Engineering
submitted by:

Libin Binu 21R01A05N6

P.Ganesh Vijay 21R01A05P7

V. Komali Reddy 21R01A05R6

Y. Anusha 21R01A05R7
Under the guidance of
Ms. Radhika
(Assistant Professor, Dept. Of Computer Science and Engineering)

CMR INSTITUTE OF TECHNOLOGY

(UGC AUTONOMOUS)
(Approved by AICTE,Affiliated to JNTU,Kukatpally,Hyderabad)
Kandlakoya,Medchal Dist- 501 401
www.cmrithyderabad.edu.in
2024-2025
 CMR INSTITUTE OF TECHNOLOGY
(UGC AUTONOMOUS)
(Approved by AICTE, Affiliated to JNTU, Kukatpally, Hyderabad) Kandlakoya,
Medchal Dist - 501 401
www.cmrithyderabad.edu.in

CERTIFICATE

This is to certify that a Mini Project entitled: “The Influence of Artificial Intelligence on
E-Governance and Cybersecurity in Smart Cities: A Stakeholder’s Perspective
” is being submitted by:

Libin Binu 21R01A05N6

P.Ganesh Vijay 21R01A05P7

V. Komali Reddy 21R01A05R6

Y. Anusha 21R01A05R7

To JNTUH, Hyderabad, in partial fulfillment of the requirement for award of the

degree of B.Tech in CSE and is a record of a bonafide work carried out under our
guidance and supervision. The results in this project have been verified and are
found to be satisfactory. The results embodied in this work have not been submitted
to have any other University for award of any other degree or diploma.

Signature of Guide Signature of Project Coordinator Signature of HOD

 ACKNOWLEDGEMENT
We are extremely grateful to Dr M. Janga Reddy, Director, Dr B. Satyanarayana, Principal,
and Mr A. Prakash, Head of Department, Dept of Computer Science and Engineering, CMR
Institute of Technology, for their inspiration and valuable guidance throughout the course.

We are extremely thankful to our Industry Oriented Mini Project faculty in charge
Ms. A Radhika, Assistant Professor, Computer Science and Engineering department, CMR
Institute of Technology for his constant guidance, encouragement and moral support throughout
the project.

We express our thanks to all staff members and friends for their help and coordination in
completing this project on time.

Finally, we are very thankful to our parents and relatives who guided us directly or indirectly for
the successful completion of the project.

Libin Binu 21R01A05N6

P.Ganesh Vijay 21R01A05P7

V. Komali Reddy 21R01A05R6

Y. Anusha 21R01A05R7
 ABSTRACT
Artificial intelligence (AI) has been identified as a critical technology of Fourth Industrial
Revolution (Industry 4.0) for protecting computer network systems against cyber-attacks,
malware, phishing, damage, or illicit access. AI has potential in strengthening the cyber
capabilities and safety of nation states, local governments, and non-state entities through
e-Governance. Existing research provides a mixed association between AI, e-Governance, and
cybersecurity; however, this relationship is believed to be context-specific. AI, e-Governance,
and cybersecurity influence and are affected by various stakeholders possessing a variety of
knowledge and expertise in respective areas. To fill this context specific gap, this study
investigates the direct relationship between AI, e-Governance, and cybersecurity. Furthermore,
this study examines the mediating role of e-Governance between AI and cybersecurity and
moderating the effect of stakeholders involvement on the relationship between AI,
e-Governance, and cybersecurity. The results of PLS-SEM path modeling analysis revealed a
partial mediating impact of e-Governance between AI and cybersecurity. Likewise, moderating
influence of stakeholders involvement was discovered on the relationship between AI and
e-Governance, as well as between e-Governance and cybersecurity. It implies that stakeholders
involvement has vital significance in AI and e-Governance because all stakeholders have interest
in vibrant, transparent, and secured cyberspace while using e-services. This study provides
practical implications for governmental bodies of smart cities for strengthening their
cybersecurity measures

ii
 INDEX
ABSTRACT ii

INDEX iii

LIST OF FIGURES iv

1. INTRODUCTION 1

2. ANALYSIS 4

2.1 EXISTING SYSTEM 4

2.2 DISADVANTAGES OF EXISTING PROJECT 5

2.3 PROPOSED PROJECT 6

2.4 ADVANTAGES OF PROPOSED PROJECT 6

2.5 SCOPE OF THE PROJECT 6

2.6 FEASIBILITY STUDY 7

3. REQUIREMENTS 9

3.1 HARDWARE REQUIREMENTS 9

3.2 SOFTWARE REQUIREMENTS 9

4. DESIGN 10

4.1 MODULE DESCRIPTION 10

4.2 UML DIAGRAMS 10

5. IMPLEMENTATION 17

6. TEST CASE 27

6.1 TESTING 27

7. CONCLUSION 32

8. REFERENCES 33

iii
 LIST OF FIGURES
Particulars Page No

Fig.4.2.1 Use Case Diagram 12

Fig.4.2.2 Sequence Diagram 13

Fig.4.2.3 Architecture Diagram 14

Fig.4.2.4 Remote User 15

Fig.4.2.5 Service Provider 16

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CHAPTER-1

Introduction
Cyber security has become a critical and vital topic that requires protecting the computer
network from potential threats in today’s modern world. A cyber-attack is a deliberate attack
targeting computer networks, relevant data, programs, and electronic information, resulting in
sub-national entities inciting violence towards noncombatant opponents. As technology
develops, so do cyber threats, necessitating the development of new prevention strategies. It has
been alleged that cyber-attacks have become more prevalent in the industrial sector, resulting in
serious infrastructure damage and significant monetary loss. The rise of cyber-attacks among
organizations is primarily due to the growing reliance on online technologies that enable the
storage of personal and economic data .

Consequently, it is acknowledged as perhaps the most critical problem in the modern

context because it creates economic loss and discloses confidential information. Cyber attacks
include phishing, denial of service, malware, and ransom ware infestations, which can harm
anybody in society . Cyber-attacks also have a significant psychological impact on humans,
producing unhappiness, tension, and stress among people .

Artificial intelligence (AI) applications can positively influence the cyber

capabilities and national security of the sovereign nation, regional government entities, and
non-state organizations . AI is a reliable technique for mitigating cyber-attack effects. AI is
machine intelligence that executes activities connected with intelligence. Human professionals’
expertise is integrated for strategic planning and decision-making, including making medical
diagnoses and getting insights from expertise in concluding. In terms of cyber security, Zarina et
al., have illustrated that AI has both beneficial and harmful effects, with the harmful effect of
facilitating the instigation phase of cyber attacks, resulting in quicker and more devastating
attacks. Looking forward, AI has the potential to greatly improve cyber security by increasing
security precautions and promoting security in cyberspace. Furthermore, AI assists security
experts in detecting cyber hazard symptoms and has enhanced the machine learning applications
for malware classification and networked intrusion detection. Lastly, the modern phenomenon in

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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AI has transformed innovative solutions and improved city external attacks against serious
security threats .

A smart city provides multiple innovative solutions to several challenges that city
administration faces. However, information and communication technology (ICT) has become a
vital component of e-Government. Implementing ICT into a city’s infrastructure introduces
hazards and obstructions . People frequently use insecure Wi-Fi networks to check their email
messages, e-banking, and other digital services, uncovering themselves to cybercrimes including
hacking, denials of service, and cracking. Cyber security applying technologies to protect
e-Government services is among the most important distinctive features that can be utilized to
categorize safe cities globally . Somewhere in this tendency, the ‘inclusive smart city’ framework
has triggered strong interest because it emphasizes the importance of interpersonal and social
capital in urban initiatives that focus on stakeholders’ inclusion in the Digital Realm and
involving inhabitants in service improvement to implement appropriate government services that
match citizens’ necessities . Recent studies on e-services and technologies also have emphasized
the importance of implementing a citizens-centered strategy for smart cities because it is
expected to develop strong social ecologies that depend strongly on web technology.
Consequently, web technologies and services can significantly impact stakeholder interactions .

Although previous literature demonstrated the influence of AI in smart mobility , energy

management , public services , climate change , and smart security in smart cities, cyber security
has widely been neglected, especially in the context of stakeholders who use online government
services. To fill this contextual gap, this study formulated the following research question:

• How AI applications used in smart cities influence cyber security directly?

• How AI applications used in smart cities influence e-Governance and e-Governance impacts
cyber security directly?

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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• Does e-Governance play a mediating role between the relationship of AI applications and cyber
security?

• Additionally, this study examines the moderating role of stakeholders’ involvement in the
relationship between AI and e-Governance and on the relationship between e-Governance and
cyber security.

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CHAPTER-2

ANALYSIS
2.1 Analysis of Existing System:

As smart cities continue to emerge worldwide, the integration of advanced technologies,

particularly Artificial Intelligence (AI), has become critical in shaping e-Governance and
cybersecurity frameworks. This analysis explores how AI affects these domains from a
stakeholder's perspective, focusing on how AI enhances the efficiency, security, and inclusivity
of digital governance systems.

1. AI’s Role in E-Governance

E-Governance refers to the use of digital tools and technologies to deliver governmental services
more efficiently to citizens. AI plays a pivotal role by automating administrative processes,
improving decision-making, and enabling predictive analytics. Some key influences of AI on
e-Governance include: AI-powered chatbots and virtual assistants streamline interactions
between citizens and government agencies, allowing for faster, more personalized service
delivery. AI enables governments to analyze vast amounts of data from smart city sensors, traffic
systems, and citizen feedback to make informed policy decisions in real time. AI-driven
platforms can enhance the transparency of governmental operations by offering insights into
budget allocations, decision-making processes, and compliance with regulations.

2.Stakeholder Involvement

Stakeholder involvement is crucial in smart cities as governments, private sectors, and citizens
are all affected by the implementation of AI in governance and security. Their perspectives offer
valuable insights: Governments benefit from improved operational efficiency and better service
delivery, while also facing the responsibility of ensuring that AI technologies are implemented
ethically and securely. Businesses gain from AI by leveraging it for smarter resource
management, while also contributing to cybersecurity innovations that protect the city’s digital
infrastructure. For citizens, AI-driven e-Governance offers greater convenience and inclusivity,

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but concerns about privacy and data security remain paramount. Active involvement from
citizens ensures transparency and accountability in AI deployments.

3.Challenges and Considerations

Despite the potential benefits, the implementation of AI in smart cities also brings several
challenges: The massive amounts of data collected by AI systems pose risks to citizen privacy.
Ensuring data protection while using AI for governance is a critical concern. The ethical
implications of using AI, including concerns about bias, discrimination, and decision-making
transparency, must be addressed. Although AI can bolster cybersecurity, it can also be exploited
by hackers to create more sophisticated attacks. Continuous advancements in AI-driven security
measures are essential.

2.2 Disadvantages of Existing Project:

1. AI's reliance on vast data increases risks of privacy breaches and inadequate data
protection.
2. AI can perpetuate biases, lack transparency, and lead to discrimination in
decision-making.
3. Over-reliance on AI can cause system vulnerabilities, reduce human oversight, and widen
the digital divide.
4. AI automation may lead to job losses and reduce citizen engagement in government
services.
5. AI can create sophisticated cybersecurity threats while also being vulnerable to hacking.
6. AI implementation is costly and requires ongoing investment in infrastructure and
expertise.
7. AI development often outpaces regulation, creating legal and accountability issues.
8. The opacity of AI systems can lead to reduced public trust in government decisions.
9. AI systems may face difficulties integrating with existing infrastructure, causing
inefficiencies.
10. AI-driven surveillance raises ethical concerns about privacy and potential overreach in
monitoring citizens.

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2.3 Proposed Project:

This study aims to explore the potential impact of Artificial Intelligence (AI) on e-Governance
and cybersecurity in smart cities, from the perspective of key stakeholders, including government
bodies, private sectors, and citizens. It investigates how AI can improve service delivery,
data-driven decision-making, and cyber threat detection, while also addressing the challenges
associated with privacy, ethical concerns, and public trust. By analyzing these dynamics, the
research seeks to provide insights into the benefits, risks, and governance strategies necessary to
ensure secure, inclusive, and efficient smart city ecosystems.

2.4 Advantages of Proposed Project:

● AI automates tasks, enabling faster and more personalized public services.

● AI analyzes data to facilitate informed policy-making and resource allocation.
● AI detects and prevents cyber threats in real time, significantly enhancing digital security.

2.5 Scope of the Project:

The scope of this study encompasses the examination of AI's impact on e-Governance and
cybersecurity within smart cities, focusing on various stakeholder perspectives, including
government entities, private sector companies, and citizens. The project aims to:

● Investigating specific AI technologies used in enhancing service delivery,

decision-making, and cybersecurity measures in urban governance.
● Analyzing the roles and experiences of different stakeholders in the adoption and
implementation of AI solutions.
● Identifying potential challenges, ethical considerations, and barriers to AI integration in
e-Governance and cybersecurity.
● Assessing the regulatory and policy frameworks necessary to support the effective and
ethical use of AI in smart cities.

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● Exploring emerging trends in AI technologies and their anticipated effects on urban
governance and security in the coming years.
● Evaluating how the effectiveness of AI implementations in e-Governance and
cybersecurity can be measured, including metrics for service efficiency, citizen
satisfaction, and security resilience.
● Examining real-world implementations of AI in various smart cities, highlighting
successful strategies and lessons learned that can inform future practices.

2.6 Feasibility Study:

The feasibility of the project is analyzed in this phase and a business proposal is put forth
with a very general plan for the project and some cost estimates. During system analysis the
feasibility study of the proposed system is to be carried out. This is to ensure that the proposed
system is not a burden to the company. For feasibility analysis, some understanding of the major
requirements for the system is essential.

Three key considerations involved in the feasibility analysis are

¨ ECONOMICAL FEASIBILITY

¨ TECHNICAL FEASIBILITY

¨ SOCIAL FEASIBILITY

ECONOMICAL FEASIBILITY:

This study is carried out to check the economic impact that the system will have on the
organization. The amount of funds that the company can pour into the research and development
of the system is limited. The expenditures must be justified. Thus the developed system as well
within the budget and this was achieved because most of the technologies used are freely
available. Only the customized products had to be purchased.

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TECHNICAL FEASIBILITY:

This study is carried out to check the technical feasibility, that is, the technical requirements of
the system. Any system developed must not have a high demand on the available technical
resources. This will lead to high demands on the available technical resources. This will lead to
high demands being placed on the client. The developed system must have a modest
requirement, as only minimal or null changes are required for implementing this system.

SOCIAL FEASIBILITY:

The aspect of study is to check the level of acceptance of the system by the user. This includes
the process of training the user to use the system efficiently. The user must not feel threatened by
the system, instead must accept it as a necessity. The level of acceptance by the users solely
depends on the methods that are employed to educate the user about the system and to make him
familiar with it. His level of confidence must be raised so that he is also able to make some
constructive criticism, which is welcomed, as he is the final user of the system.

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CHAPTER-3

REQUIREMENTS

3.System Specification:

3.1Hardware System Configuration:

● Processor - Pentium –IV

● RAM - 4 GB (min)
● Hard Disk - 20 GB
● Key Board - Standard Windows Keyboard
● Mouse - Two or Three Button Mouse
● Monitor - SVGA

3.2 Software Requirements:

● Operating system : Windows 7 Ultimate.

● Coding Language : Python.
● Front-End : Python.
● Back-End : Django-ORM
● Designing : Html, CSS, JavaScript.
● Data Base : MySQL (WAMP Server).

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CHAPTER-4

DESIGN
4.1 Module Description:

● Understand AI fundamentals and their relevance to e-Governance and cybersecurity.

● Analyze stakeholder roles and their contributions to AI adoption.
● Identify challenges and ethical considerations in AI integration.
● Explore technical requirements for effective AI deployment.
● Evaluate the social and economic impacts of AI on urban dynamics.
● Overview of AI applications in urban governance.
● Enhancing service delivery and citizen engagement through AI.
● Role of AI in threat detection and security measures.
● Identifying and analyzing key stakeholders in AI initiatives.
● Addressing data privacy, bias, and the digital divide.
● Software and hardware needs for AI implementation.
● Analyzing the effects of AI on communities and job markets.
● Exploring emerging AI technologies and their implications for governance.

4.2 UML Diagrams:

UML is an acronym that stands for Unified Modeling Language. Simply put, UML is a modern
approach to modeling and documenting software. In fact, it’s one of the most popular business
process modeling techniques. It is based on diagrammatic representations of software
components. As the old proverb says: “a picture is worth a thousand words”. By using visual
representations, we are able to better understand possible flaws or errors in software or business
processes. UML was created as a result of the chaos revolving around software development and
documentation. In the 1990s, there were several different ways to represent and document
software systems. The need arose for a more unified way to visually represent those systems and
as a result, in 1994-1996, the UML was developed by three software engineers working at

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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Rational Software. It was later adopted as the standard in 1997 and has remained the standard
ever since, receiving only a few updates.

Goals:

The Primary goals in the design of the UML are as follows:

1. Provide users a ready-to-use, expressive visual modeling Language so that they can develop
and exchange meaningful models.

2. Provide extensibility and specialization mechanisms to extend the core concepts.

3. Be independent of particular programming languages and development process.

4. Provide a formal basis for understanding the modeling language.

5. Encourage the growth of OO tools market.

6. Support higher level development concepts such as collaborations, frameworks, patterns and
components.

7. Integrate best practice.

1.Use Case Diagram:

A use case diagram in the Unified Modeling Language (UML) is a type of behavioral diagram
defined by and created from a Use-case analysis. Its purpose is to present a graphical overview
of the functionality provided by a system in terms of actors, their goals (represented as use
cases), and any dependencies between those use cases. The main purpose of a use case diagram
is to show what system functions are performed for which actor. Roles of the actors in the system
can be depicted.

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Fig.4.2.1 Use Case Diagram

2.Sequence Diagram:

A sequence diagram in Unified Modeling Language (UML) is a kind of interaction diagram that
shows how processes operate with one another and in what order. It is a construct of a Message
Sequence Chart. Sequence diagrams are sometimes called event diagrams, event scenarios, and
timing diagrams.
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Fig 4.2.2 Sequence Diagram

3.Architecture Diagram:

An Architecture Diagram is a visual representation of a system’s architecture, illustrating how its

components interact, communicate, and are structured. It serves as a blueprint for designing and
understanding the system, making it easier for stakeholders—such as developers, architects, and
project managers—to grasp the overall design and functionality. Here are the key elements
typically included in an architecture diagram

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Fig 4.2.3 Architecture Diagram

4.Flow Chart Diagram:

A Flow Chart Diagram is a visual representation of a process, illustrating the sequence of steps
or actions involved in completing a specific task or achieving a particular goal. Flow charts use
standardized symbols and connecting lines to depict the flow of information, decisions, and
actions, making it easier to understand and analyze complex processes.

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Remote User

Fig 4.2.4 Flow chart Diagram (Remote User)

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Service Provider

Fig 4.2.5 Flow chart Diagram (Service Provider)

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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CHAPTER-5

IMPLEMENTATION
5.1 Source Code:

Remote User’s View File:

from django.db.models import Count

from django.db.models import Q

from django.shortcuts import render, redirect, get_object_or_404

import pandas as pd

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

from sklearn.metrics import accuracy_score

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import VotingClassifier

# Create your views here.

from Remote_User.models import

ClientRegister_Model,cyber_attack_detection,detection_ratio,detection_accuracy

def login(request):

if request.method == "POST" and 'submit1' in request.POST:

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username = request.POST.get('username')

password = request.POST.get('password')

try:

enter = ClientRegister_Model.objects.get(username=username,password=password)

request.session["userid"] = enter.id

return redirect('ViewYourProfile')

except:

pass

return render(request,'RUser/login.html')

def index(request):

return render(request, 'RUser/index.html')

def Add_DataSet_Details(request):

return render(request, 'RUser/Add_DataSet_Details.html', {"excel_data": ''})

def Register1(request):

if request.method == "POST":

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username = request.POST.get('username')

email = request.POST.get('email')

password = request.POST.get('password')

phoneno = request.POST.get('phoneno')

country = request.POST.get('country')

state = request.POST.get('state')

city = request.POST.get('city')

address = request.POST.get('address')

gender = request.POST.get('gender')

ClientRegister_Model.objects.create(username=username, email=email,
password=password, phoneno=phoneno,

country=country, state=state,
city=city,address=address,gender=gender)

obj = "Registered Successfully"

return render(request, 'RUser/Register1.html',{'object':obj})

else:

return render(request,'RUser/Register1.html')

def ViewYourProfile(request):

userid = request.session['userid']

obj = ClientRegister_Model.objects.get(id= userid)

return render(request,'RUser/ViewYourProfile.html',{'object':obj})

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def Predict_Cyber_Attack_Type(request):

if request.method == "POST":

if request.method == "POST":

Fid= request.POST.get('Fid')

Timestamp= request.POST.get('Timestamp')

Source_IP_Address= request.POST.get('Source_IP_Address')

Destination_IP_Address= request.POST.get('Destination_IP_Address')

Source_Port= request.POST.get('Source_Port')

Destination_Port= request.POST.get('Destination_Port')

Protocol= request.POST.get('Protocol')

Packet_Length= request.POST.get('Packet_Length')

Packet_Type= request.POST.get('Packet_Type')

Traffic_Type= request.POST.get('Traffic_Type')

Payload_Data= request.POST.get('Payload_Data')

Malware_Indicators= request.POST.get('Malware_Indicators')

Anomaly_Scores= request.POST.get('Anomaly_Scores')

Alerts_Warnings= request.POST.get('Alerts_Warnings')

Attack_Signature= request.POST.get('Attack_Signature')

Action_Taken= request.POST.get('Action_Taken')

Severity_Level= request.POST.get('Severity_Level')

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Device_Information= request.POST.get('Device_Information')

Network_Segment= request.POST.get('Network_Segment')

Geo_City_location_Data= request.POST.get('Geo_City_location_Data')

Proxy_Information= request.POST.get('Proxy_Information')

Firewall_Logs= request.POST.get('Firewall_Logs')

IDS_IPS_Alerts= request.POST.get('IDS_IPS_Alerts')

Log_Source= request.POST.get('Log_Source')

df = pd.read_csv('Datasets.csv')

def apply_response(label):

if (label == 'Malware'):

return 0 # Malware

elif (label == 'DDoS'):

return 1 # DDoS

elif (label == 'Intrusion'):

return 2 # Intrusion

df['results'] = df['Attack_Type'].apply(apply_response)

cv = CountVectorizer()

X = df['Fid']

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y = df['results']

print("Fid")

print(X)

print("Results")

print(y)

X = cv.fit_transform(X)

models = []

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)

X_train.shape, X_test.shape, y_train.shape

print("Naive Bayes")

from sklearn.naive_bayes import MultinomialNB

NB = MultinomialNB()

NB.fit(X_train, y_train)

predict_nb = NB.predict(X_test)

naivebayes = accuracy_score(y_test, predict_nb) * 100

print("ACCURACY")

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print(naivebayes)

print("CLASSIFICATION REPORT")

print(classification_report(y_test, predict_nb))

print("CONFUSION MATRIX")

print(confusion_matrix(y_test, predict_nb))

models.append(('naive_bayes', NB))

# SVM Model

print("SVM")

from sklearn import svm

lin_clf = svm.LinearSVC()

lin_clf.fit(X_train, y_train)

predict_svm = lin_clf.predict(X_test)

svm_acc = accuracy_score(y_test, predict_svm) * 100

print("ACCURACY")

print(svm_acc)

print("CLASSIFICATION REPORT")

print(classification_report(y_test, predict_svm))

print("CONFUSION MATRIX")

print(confusion_matrix(y_test, predict_svm))

models.append(('svm', lin_clf))

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print("Logistic Regression")

from sklearn.linear_model import LogisticRegression

reg = LogisticRegression(random_state=0, solver='lbfgs').fit(X_train, y_train)

y_pred = reg.predict(X_test)

print("ACCURACY")

print(accuracy_score(y_test, y_pred) * 100)

print("CLASSIFICATION REPORT")

print(classification_report(y_test, y_pred))

print("CONFUSION MATRIX")

print(confusion_matrix(y_test, y_pred))

models.append(('logistic', reg))

classifier = VotingClassifier(models)

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

Fid1 = [Fid]

vector1 = cv.transform(Fid1).toarray()

predict_text = classifier.predict(vector1)

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pred = str(predict_text).replace("[", "")

pred1 = pred.replace("]", "")

prediction = int(pred1)

if (prediction == 0):

val = 'Malware'

elif (prediction == 1):

val = 'DDoS'

elif (prediction == 2):

val = 'Intrusion'

print(val)

print(pred1)

cyber_attack_detection.objects.create(

Fid=Fid,

Timestamp=Timestamp,

Source_IP_Address=Source_IP_Address,

Destination_IP_Address=Destination_IP_Address,

Source_Port=Source_Port,

Destination_Port=Destination_Port,

Protocol=Protocol,

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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Packet_Length=Packet_Length,

Packet_Type=Packet_Type,

Traffic_Type=Traffic_Type,

Payload_Data=Payload_Data,

Malware_Indicators=Malware_Indicators,

Anomaly_Scores=Anomaly_Scores,

Alerts_Warnings=Alerts_Warnings,

Attack_Signature=Attack_Signature,

Action_Taken=Action_Taken,

Severity_Level=Severity_Level,

Device_Information=Device_Information,

Network_Segment=Network_Segment,

Geo_City_location_Data=Geo_City_location_Data,

Proxy_Information=Proxy_Information,

Firewall_Logs=Firewall_Logs,

IDS_IPS_Alerts=IDS_IPS_Alerts,

Log_Source=Log_Source,

Prediction=val)

return render(request, 'RUser/Predict_Cyber_Attack_Type.html',{'objs': val})

return render(request, 'RUser/Predict_Cyber_Attack_Type.html')

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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CHAPTER-6

TEST CASE

6.1 Testing:
The purpose of testing is to discover errors. Testing is the process of trying to discover every
conceivable fault or weakness in a work product. It provides a way to check the functionality of
components, subassemblies, assemblies and/or a finished product. It is the process of exercising
software with the intent of ensuring that the Software system meets its requirements and user
expectations and does not fail in an unacceptable manner. There are various types of tests. Each
test type addresses a specific testing requirement.

Fig 6.1.1

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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Fig 6.1.2

Fig 6.1.3

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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Fig 6.1.4

Fig 6.1.5

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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Fig 6.1.6

Fig 6.1.7

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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Fig 6.1.8

Fig 6.1.9

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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A
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CHAPTER-7

CONCLUSION
The current study examined artificial intelligence applications to overcome cyber security
challenges. The research findings indicate that artificial intelligence is progressively converting
into an indispensable technology to enhance information security performance. Individuals are
not capable of fully secure project-level cyber attacks, and artificial intelligence offers the
desired analytics and threat intelligence that security practitioners might use to minimize the
likelihood of an infringement and strengthen the security structure of an enterprise. Since more
technologies computing in cyber security is the capacity to evaluate and eliminate risk faster.
Several individuals are concerned about cybercriminals’ capability to perform incredibly
advanced cyber and technological attacks. Moreover, artificial intelligence can contribute to the
detection and classification of hazards, the structuring of incident management, and the detection
of cyber attacks before their occurrence. Consequently, despite potential negatives, artificial
intelligence would contribute to the evolution of cyber security and support enterprises in
establishing an enhanced security strategy. This study further sought to investigate artificial
intelligence and its ongoing development in offering e-government services and then highlight
the need to accommodate strategies regarding cyber security for adopting innovative social and
technical processes in government serving the community. The eventual objective of smart city
governments is to establish and strengthen relationships with most stakeholders, as their
involvement strengthens e-government efficacy which fortifies cyber security. Public services
should be administered using innovative AI technologies and e-governance in convenient modes
to eliminate the barriers between stakeholders and city governments, while state officials can still
sustain the model for better support. While e-government is progressing, the citizens and those in
authority or advocating mechatronics are lagging. That creates disparities in cyber security
standards for something in the virtual environment, potentially turning performance into a much
more difficult experience with several grooves to monitor. With an elevation in the initiatives
identified in this research, stakeholders’ involvement and awareness of e-governance and cyber
security may rise, enabling benefits associated with the virtual environment.

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CHAPTER-8

REFERENCES
[1] B. Alhayani, H. J. Mohammed, I. Z. Chaloob, and J. S. Ahmed, ‘‘Effective artificial
intelligence techniques against cyber security risks apply to the IT industry,’’ Mater. Today,
Proc., vol. 531, pp. 1–6, 2021, doi: 10.1016/j.matpr.2021.02.531.

[2] M. Komar, V. Kochan, L. Dubchak, A. Sachenko, V. Golovko, S. Bezobrazov, and I.

Romanets, ‘‘High performance adaptive system for cyber attacks detection,’’ in Proc. 9th IEEE
Int. Conf. Intell. Data Acquisition Adv. Compute. Syst., Technol. Appl. (IDAACS), vol. 2, Sep.
2017, pp. 853–858.

[3] M. D. Cavelty, Cyber-Security and Threat Politics: US Efforts to Secure the Information Age.
Evanston, IL, USA: Routledge, 2007.

[4] F. Fransen, A. Smulders, R. Kerkdijk, ‘‘Cyber security information exchange to gain insight
into the effects of cyber threats and incidents,’’ Elektrotechnik Informationstechnik, vol. 132, no.
2, pp. 106–112, Mar. 2015.

[5] A. Corallo, M. Lazoi, M. Lezzi, and A. Luperto, ‘‘Cybersecurity awareness in the context of
the industrial Internet of Things: A systematic literature review,’’ Comput. Ind., vol. 137, May
2022, Art. no. 103614.

[6] G. A.Weaver, B. Feddersen, L. Marla, D.Wei, A. Rose, and M. Van Moer, ‘‘Estimating
economic losses from cyber-attacks on shipping ports: An optimization-based approach,’’
Transp. Res. C, Emerg. Technol., vol. 137, Apr. 2022, Art. no. 103423.

[7] M. Bada and J. R. C. Nurse, ‘‘The social and psychological impact of cyberattacks,’’ in
Emerging Cyber Threats and Cognitive Vulnerabilities. Amsterdam, The Netherlands: Elsevier,
2020, pp. 73–92. [8] G. Allen and T. Chan, Artificial Intelligence and National Security.
Cambridge, MA, USA: Belfer Center for Science and International Affairs, 2017.

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