Theses and Dissertations
Advisor
Manias, Dimitrios
Committee Member
Chaudhary, Vini
Committee Member
Trawick, George
Date of Degree
5-15-2026
Original embargo terms
Embargo 6 months
Document Type
Graduate Thesis - Open Access
Major
Cybersecurity & Operations
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Computer Science and Engineering
Abstract
Along with the recent rise in popularity of Electric Vehicles (EVs), Electric Vehicle Supply Equipment (EVSE) has emerged as a new target for cyber attacks. Therefore, ensuring the security and integrity of network communication between EVSE components and vehicular clients is a significant challenge that must be addressed. To this end, this thesis analyzes the state of the art in machine learning-based EVSE attack detection models to identify shortcomings in current approaches. To address these shortcomings, the Flow-based Analysis and Labeling for COnnected vehicular Network Cybersecurity (FALCON-C) framework is proposed. FALCON-C leverages an autoencoder for anomaly detection and is trained on a small number of benign flows from the CICEVSE2024 dataset. By modeling benign flow behavior, malicious flows are detected by identifying statistically different reconstruction error profiles. The model achieves 100% accuracy against malicious data, and with a refined decision boundary, achieves 96.92% accuracy against the benign training data.
Recommended Citation
Bean, Joshua, "Labeling anomalous network flows for cyber attack detection in electric vehicle charging infrastructure" (2026). Theses and Dissertations. 6872.
https://scholarsjunction.msstate.edu/td/6872
