A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles. (November 2022)

• Record Type: Journal Article
• Authors: Ilango, Harun Surej; Ma, Maode; Su, Rong
• Is Part Of: Engineering applications of artificial intelligence. Volume 116(2022)
• Publication Date: 2022-11
• Subjects: AE AutoEncoder -- AMF Access and Mobility Management Function -- AUC Area Under the Curve -- BSM Basic Safety Message -- CA Certificate Authority -- CCR Correct Classification Rate -- DSRC Dedicated Short Range Communications -- FN-BSMD Fog Node - Basic Safety Message Database -- IoV Internet of Vehicles -- MCR Misclassification Rate -- MDS Misbehavior Detection System -- MSE Mean Squared Error -- MVD Misbehaving Vehicles Database -- NADM Novel Attack Detection Module -- NASEA Novel Attack Sample Extraction Ability -- NPFADS for the IoV Novel Position Falsification Attack Detection System for the Internet of Vehicles -- OBU OnBoard Unit -- OBU-BSMD OnBoard Unit - Basic Safety Message Database -- RF Random Forest -- ROC Receiver Operating Characteristic -- RSSI Received Signal Strength Indicator -- RSU Road Side Unit -- V2I Vehicle-to-Infrastructure -- V2V Vehicle-to-Vehicle -- VeReMi Vehicular Reference Misbehavior; Internet of Vehicles -- Position falsification attacks -- Machine learning -- Novel attack detection -- Basic Safety Messages (BSMs) -- VeReMi dataset -- Vehicle-to-Vehicle (V2V) communication -- Anomaly detection; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105380
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In the Internet of Vehicle (IoV) networks, vehicles exchange periodic Basic Safety Messages (BSMs) containing information regarding speed and position. Safety-critical applications like blind-spot warning and lane change warning systems use the BSMs to ensure the safety of road users. To create chaos in the network, an insider attacker may inject false information into the BSM and broadcast it to nearby vehicles. One such attack is the position falsification attack, where the attacker inserts false information regarding the position in the BSMs. The literature has explored the use of Misbehavior Detection Systems (MDSs) to detect such attacks. But the limitaitons of the existing approaches are that they either perform exceptionally well in specific environmental settings or have compromised detection accuracy favoring a generalized model. Moreover, all the current machine learning-based detection models are signature-based, which requires prior knowledge about the attacks for effective detection. Motivated by the research gap, we propose a Novel Position Falsification Attack Detection System for the Internet of Vehicles (NPFADS for the IoV) to learn and detect novel position falsification attacks emerging in IoV networks. The performance of NPFADS is analyzed using the metrics precision, recall, F1 score, ROC, and PR curves. The Vehicular Reference Misbehavior (VeReMi) dataset is used as the benchmark for the study. The system's performance is compared to existing MDSs in the literature. The analysis shows that our proposed system outperforms the existing supervised learning models even when initialized with zero knowledge about the novel position falsification attacks.