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Title: RSU-Based Online Intrusion Detection and Mitigation for VANET
Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability.  more » « less
Award ID(s):
2040572
NSF-PAR ID:
10418528
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Sensors
Volume:
22
Issue:
19
ISSN:
1424-8220
Page Range / eLocation ID:
7612
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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