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Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

Using discriminant analysis to detect intrusions in external communication for self-driving vehicles
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摘要 Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS) to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DOS) and black hole attacks on vehicular ad hoe networks (VANETs). The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA) and Quadratic Diseriminant Analysis (QDA) which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.
出处 《Digital Communications and Networks》 SCIE 2017年第3期180-187,共8页 数字通信与网络(英文版)
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