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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7612 |
| _version_ | 1827652973073793024 |
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| author | Ammar Haydari Yasin Yilmaz |
| author_facet | Ammar Haydari Yasin Yilmaz |
| author_sort | Ammar Haydari |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>78</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the best case and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>27</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the worst case, while achieving the same level of false alarm probability. |
| first_indexed | 2024-03-09T21:10:33Z |
| format | Article |
| id | doaj.art-e02cc3d425624514a0a3d92528746235 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:33Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e02cc3d425624514a0a3d925287462352023-11-23T21:52:01ZengMDPI AGSensors1424-82202022-10-012219761210.3390/s22197612RSU-Based Online Intrusion Detection and Mitigation for VANETAmmar Haydari0Yasin Yilmaz1Electrical Engineering Department, University of South Florida, Tampa, FL 33620, USAElectrical Engineering Department, University of South Florida, Tampa, FL 33620, USASecure 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>78</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the best case and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>27</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the worst case, while achieving the same level of false alarm probability.https://www.mdpi.com/1424-8220/22/19/7612vehicular ad-hoc networksstatistical anomaly detectionmachine learningfalse data injection attackDDoS attackroad side unit |
| spellingShingle | Ammar Haydari Yasin Yilmaz RSU-Based Online Intrusion Detection and Mitigation for VANET Sensors vehicular ad-hoc networks statistical anomaly detection machine learning false data injection attack DDoS attack road side unit |
| title | RSU-Based Online Intrusion Detection and Mitigation for VANET |
| title_full | RSU-Based Online Intrusion Detection and Mitigation for VANET |
| title_fullStr | RSU-Based Online Intrusion Detection and Mitigation for VANET |
| title_full_unstemmed | RSU-Based Online Intrusion Detection and Mitigation for VANET |
| title_short | RSU-Based Online Intrusion Detection and Mitigation for VANET |
| title_sort | rsu based online intrusion detection and mitigation for vanet |
| topic | vehicular ad-hoc networks statistical anomaly detection machine learning false data injection attack DDoS attack road side unit |
| url | https://www.mdpi.com/1424-8220/22/19/7612 |
| work_keys_str_mv | AT ammarhaydari rsubasedonlineintrusiondetectionandmitigationforvanet AT yasinyilmaz rsubasedonlineintrusiondetectionandmitigationforvanet |