A MACHINE LEARNING-BASED INTRUSION DETECTION SYSTEM FOR IOT ELECTRIC VEHICLE CHARGING STATIONS (EVCS)/ (Record no. 10038)

MARC details
000 -LEADER
fixed length control field 08043nam a22002537a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201210b2023 a|||f bm|| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-CaNU
Transcribing agency EG-CaNU
041 0# - Language Code
Language code of text eng
Language code of abstract eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 658
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Mohamed Ahmed Hassan ElKashlan
245 1# - TITLE STATEMENT
Title A MACHINE LEARNING-BASED INTRUSION DETECTION SYSTEM FOR IOT ELECTRIC VEHICLE CHARGING STATIONS (EVCS)/
Statement of responsibility, etc. Mohamed Ahmed Hassan ElKashlan
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2023
300 ## - PHYSICAL DESCRIPTION
Extent 68 p.
Other physical details ill.
Dimensions 21 cm.
500 ## - GENERAL NOTE
Materials specified Supervisor: <br/>Heba Aslan
502 ## - Dissertation Note
Dissertation type Thesis (M.A.)—Nile University, Egypt, 2023 .
504 ## - Bibliography
Bibliography "Includes bibliographical references"
505 0# - Contents
Formatted contents note Contents:<br/>Contents<br/>CERTIFICATION OF APPROVAL.................................................................................iii<br/>DEDICATION.................................................................................................................. vii<br/>ACKNOWLEDGEMENTS............................................................................................... ix<br/>TABLE OF CONTENTS................................................................................................... xi<br/>LIST OF FIGURES ......................................................................................................... xiii<br/>LIST OF TABLES............................................................................................................ xv<br/>ACRONYMS.................................................................................................................. xvii<br/>PUBLICATIONS............................................................................................................. xix<br/>ABSTRACT..................................................................................................................... xxi<br/>CHAPTER 1 INTRODUCTION ........................................................................................ 1<br/>1.1 Introduction....................................................................................................1<br/>1.2 Problem definition .........................................................................................2<br/>1.3 Motivation......................................................................................................4<br/>1.4 Intrusion detection in IoT...............................................................................5<br/>1.5 Thesis Contributions......................................................................................7<br/>CHAPTER 2 BACKGROUND .......................................................................................... 8<br/>2.1 EVCS background .........................................................................................8<br/>2.2.1 Attacks on the EVCS ................................................................................10<br/>2.2.2 Users’ Attacks...........................................................................................11<br/>xii<br/>2.2.3 Power Grid Attacks...................................................................................12<br/>2.3 EVCS and IoT..............................................................................................14<br/>CHAPTER 3 RELATED WORK..................................................................................... 19<br/>3.1 Related Research..........................................................................................19<br/>3.2 Literature review..........................................................................................20<br/>CHAPTER 4 METHODOLOGY ..................................................................................... 31<br/>4.1. Introduction.................................................................................................31<br/>4.2. Dataset description......................................................................................33<br/>4.3. IoT-23 Dataset ............................................................................................33<br/>4.4. Selected features .........................................................................................35<br/>4.5. Machine Learning Classifiers.....................................................................41<br/>4.6. Experimental setup......................................................................................48<br/>CHAPTER 5 SIMULATION ........................................................................................... 51<br/>5.1 Evaluation Metrics.......................................................................................51<br/>5.1.1 Confusion matrix ......................................................................................51<br/>5.1.2 Precision....................................................................................................52<br/>5.1.3 Accuracy ...................................................................................................52<br/>5.1.4 Recall Score ..............................................................................................52<br/>5.1.5 F-1 Score...................................................................................................52<br/>5.2. Experimental Results..................................................................................53<br/>5.2.1 Binary Classification results.....................................................................55<br/>5.2.2 Multiclass classification results ................................................................57<br/>CHAPTER 6 CONCLUSION AND FUTURE WORK ................................................... 62<br/>6.1 Discussion and Limitations..........................................................................62<br/>6.2 Conclusion and Future Work.......................................................................64<br/>REFERENCES ................................................................................................................. 65
520 3# - Abstract
Abstract Abstract:<br/>The market for Electric Vehicles (EVs) has expanded tremendously as seen in the <br/>recent Conference of the Parties 27 (COP27) held at Sharm El Sheikh, Egypt in <br/>November 2022. This needs the creation of an ecosystem that is user-friendly and <br/>secure. Internet connected Electric Vehicle Charging Stations (EVCSs) provide <br/>rich user experience and add-on services. Eventually, the EVCSs are connected to <br/>a management system, which is the Electric Vehicle Charging Station <br/>Management System (EVCSMS). Attacking the EVCS ecosystem is rising at the <br/>same rate as those of physical attacks and vandalism happening on the physical <br/>EVCSs. The cyberattack is more severe than the physical attack as it may affect <br/>thousands of EVCSs at the same time. Intrusion Detection is vital in defending <br/>against diverse types of attacks and unauthorized activities. Fundamentally, the <br/>Intrusion Detection System’s (IDS) problem is a classification problem. The IDS <br/>tries to find out if each traffic stream is legitimate or malicious. Furthermore, the <br/>IDS can identify the type of malicious traffic. In this study, we address IoT <br/>security issues in EVCS, by using different machine learning techniques that were <br/>created to address other, non-IoT security challenges. We also compare different <br/>machine learning classifier algorithms for detecting different kinds of attacks in <br/>the EVCS network environment. A typical Internet of Things (IoT) dataset <br/>obtained from actual IoT traffic is used in the study. We compare classification <br/>algorithms that are used in the Intrusion detection systems that are placed inline to <br/>xxii<br/>the traffic. This traffic contains different types of attacks targeting the EVCS <br/>network. The results obtained from this research improve the stability of the <br/>EVCS system and significantly reduce the amount of cyberattacks that could <br/>disrupt the daily life activities associated with the EVCS ecosystem
546 ## - Language Note
Language Note Text in English, abstracts in English.
650 #4 - Subject
Subject Information Security
655 #7 - Index Term-Genre/Form
Source of term NULIB
focus term Dissertation, Academic
690 ## - Subject
School Information Security
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
650 #4 - Subject
-- 294
655 #7 - Index Term-Genre/Form
-- 187
690 ## - Subject
-- 294
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total Checkouts Full call number Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Main library Main library 06/05/2023   658 /M.E.M / 2023 06/05/2023 06/05/2023 Thesis