Statistical Anomaly Detection and Mitigation of Cyber Attacks for Intelligent Transportation Systems
Graduation Year
2019
Document Type
Thesis
Degree
M.S.E.E.
Degree Name
MS in Electrical Engineering (M.S.E.E.)
Degree Granting Department
Electrical Engineering
Major Professor
Yasin Yilmaz, Ph.D.
Committee Member
Ismail Uysal, Ph.D.
Committee Member
Kwang-Cheng Chen, Ph.D.
Keywords
DDoS attack, false data injection, intrusion detection, security objectives, VANET security
Abstract
Secure vehicular communication is a critical factor for secure traffic management. Perfect security in intelligent transportation systems (ITS) has solid and efficient intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-ofservice attacks (DDoS), especially the stealth low-rate DDoS attacks, targeting the integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel statistical intrusion detection and mitigation techniques are proposed for the considered attacks. The performance of the proposed methods are evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior performance of the proposed methods in terms of quick and accurate detection and localization of cyber-attacks.
Scholar Commons Citation
Haydari, Ammar, "Statistical Anomaly Detection and Mitigation of Cyber Attacks for Intelligent Transportation Systems" (2019). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/8369
