Abstract
Vehicular Ad hoc NETworks (VANETs) serve as the backbone of Intelligent Transportation Systems (ITS), providing passengers with safety and comfort. However, VANETs are vulnerable to major threats that affect data privacy and network services either from an individual or distributed attacker. In this paper, a Secure and Private-Collaborative Intrusion Detection System (SP-CIDS) is proposed to detect network attacks and to mitigate security concerns. In SP-CIDS, a Distributed Machine Learning (DML) model based on the Alternating Direction Method of Multipliers (ADMM) is used, which leverages the potential of vehicle-to-vehicle collaboration in the learning process to improve the storage efficiency, accuracy, and scalability of the IDS. However, there are significant data privacy concerns possible in such collaboration, where a CIDS can act as a malicious system that has access to the intermediate stages of the learning process. Additionally, the SP-CIDS system uses Differential Privacy (DP) technique to address the aforementioned data privacy risk associated with the DML-based CIDS. The SP-CIDS system is evaluated with logistic regression, naïve bayes, and ensemble classifiers. Simulation results substantiate that a private ensemble classifier secures the training data with DP and also achieves 96.94% accuracy.
Published Version
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