A MACHINE LEARNING-BASED INTRUSION DETECTION SYSTEM FOR IOT ELECTRIC VEHICLE CHARGING STATIONS (EVCS)/
Mohamed Ahmed Hassan ElKashlan
- 2023
- 68 p. ill. 21 cm.
Supervisor: Heba Aslan
Thesis (M.A.)—Nile University, Egypt, 2023 .
"Includes bibliographical references"
Contents: Contents CERTIFICATION OF APPROVAL.................................................................................iii DEDICATION.................................................................................................................. vii ACKNOWLEDGEMENTS............................................................................................... ix TABLE OF CONTENTS................................................................................................... xi LIST OF FIGURES ......................................................................................................... xiii LIST OF TABLES............................................................................................................ xv ACRONYMS.................................................................................................................. xvii PUBLICATIONS............................................................................................................. xix ABSTRACT..................................................................................................................... xxi CHAPTER 1 INTRODUCTION ........................................................................................ 1 1.1 Introduction....................................................................................................1 1.2 Problem definition .........................................................................................2 1.3 Motivation......................................................................................................4 1.4 Intrusion detection in IoT...............................................................................5 1.5 Thesis Contributions......................................................................................7 CHAPTER 2 BACKGROUND .......................................................................................... 8 2.1 EVCS background .........................................................................................8 2.2.1 Attacks on the EVCS ................................................................................10 2.2.2 Users’ Attacks...........................................................................................11 xii 2.2.3 Power Grid Attacks...................................................................................12 2.3 EVCS and IoT..............................................................................................14 CHAPTER 3 RELATED WORK..................................................................................... 19 3.1 Related Research..........................................................................................19 3.2 Literature review..........................................................................................20 CHAPTER 4 METHODOLOGY ..................................................................................... 31 4.1. Introduction.................................................................................................31 4.2. Dataset description......................................................................................33 4.3. IoT-23 Dataset ............................................................................................33 4.4. Selected features .........................................................................................35 4.5. Machine Learning Classifiers.....................................................................41 4.6. Experimental setup......................................................................................48 CHAPTER 5 SIMULATION ........................................................................................... 51 5.1 Evaluation Metrics.......................................................................................51 5.1.1 Confusion matrix ......................................................................................51 5.1.2 Precision....................................................................................................52 5.1.3 Accuracy ...................................................................................................52 5.1.4 Recall Score ..............................................................................................52 5.1.5 F-1 Score...................................................................................................52 5.2. Experimental Results..................................................................................53 5.2.1 Binary Classification results.....................................................................55 5.2.2 Multiclass classification results ................................................................57 CHAPTER 6 CONCLUSION AND FUTURE WORK ................................................... 62 6.1 Discussion and Limitations..........................................................................62 6.2 Conclusion and Future Work.......................................................................64 REFERENCES ................................................................................................................. 65
Abstract: The market for Electric Vehicles (EVs) has expanded tremendously as seen in the recent Conference of the Parties 27 (COP27) held at Sharm El Sheikh, Egypt in November 2022. This needs the creation of an ecosystem that is user-friendly and secure. Internet connected Electric Vehicle Charging Stations (EVCSs) provide rich user experience and add-on services. Eventually, the EVCSs are connected to a management system, which is the Electric Vehicle Charging Station Management System (EVCSMS). Attacking the EVCS ecosystem is rising at the same rate as those of physical attacks and vandalism happening on the physical EVCSs. The cyberattack is more severe than the physical attack as it may affect thousands of EVCSs at the same time. Intrusion Detection is vital in defending against diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System’s (IDS) problem is a classification problem. The IDS tries to find out if each traffic stream is legitimate or malicious. Furthermore, the IDS can identify the type of malicious traffic. In this study, we address IoT security issues in EVCS, by using different machine learning techniques that were created to address other, non-IoT security challenges. We also compare different machine learning classifier algorithms for detecting different kinds of attacks in the EVCS network environment. A typical Internet of Things (IoT) dataset obtained from actual IoT traffic is used in the study. We compare classification algorithms that are used in the Intrusion detection systems that are placed inline to xxii the traffic. This traffic contains different types of attacks targeting the EVCS network. The results obtained from this research improve the stability of the EVCS system and significantly reduce the amount of cyberattacks that could disrupt the daily life activities associated with the EVCS ecosystem