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For in-vehicle network communication, the controller area network (CAN) broadcasts to all connected nodes without address validation. Therefore, it is highly vulnerable to all sorts of attack scenarios. This research proposes a novel intrusion detection system (IDS) for CAN to identify in-vehicle network anomalies. The statistical characteristics of attacks provide valuable information about the inherent intrusion patterns and behaviors. We employed two real-world attack scenarios from publicly available datasets to record a real-time response against intrusions with increased precision for in-vehicle network environments. Our proposed IDS can exploit malicious patterns by calculating thresholds and using the statistical properties of attacks, making attack detection more efficient. The optimized threshold value is calculated using brute-force optimization for various window sizes to minimize the total error. The reference values of normality require a few legitimate data frames for effective intrusion detection. The experimental findings validate that our suggested method can efficiently detect fuzzy, merge, and denial-of-service (DoS) attacks with low false-positive rates. It is also demonstrated that the total error decreases with an increasing attack rate for varying window sizes. The results indicate that our proposed IDS minimizes the misclassification rate and is hence better suited for in-vehicle networks.

Intrusion Detection System CAN-Bus In-Vehicle Networks Based on the Statistical Characteristics of Attacks