Journal
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
Volume 4, Issue 1, Pages 148-161Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSIPN.2018.2801622
Keywords
ADMM; data processing; differential privacy; distributed computing; machine learning; network security; vehicle safety; vehicular ad hoc networks
Funding
- National Science Foundation [CNS-1544782, CNS-1720230]
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Vehicular ad hoc network (VANET) is an enabling technology in modern transportation systems for providing safety and valuable information, and yet vulnerable to a number of attacks from passive eavesdropping to active interfering. Intrusion detection systems (IDSs) are important devices that can mitigate the threats by detecting malicious behaviors. Furthermore, the collaborations among vehicles in VANETs can improve the detection accuracy by communicating their experiences between nodes. To this end, distributed machine learning is a suitable framework for the design of scalable and implementable collaborative detection algorithms over VANETs. One fundamental barrier to collaborative learning is the privacy concern as nodes exchange data among them. A malicious node can obtain sensitive information of other nodes by inferring from the observed data. In this paper, we propose a privacy-preserving machine-learning based collaborative IDS (PML-CIDS) for VANETs. The proposed algorithm employs the alternating direction method of multipliers to a class of empirical risk minimization problems and trains a classifier to detect the intrusions in the VANETs. We use the differential privacy to capture the privacy notation of the PML-CIDS and propose amethod of dual-variable perturbation to provide dynamic differential privacy. We analyze theoretical performance and characterize the fundamental tradeoff between the security and privacy of the PML-CIDS. We also conduct numerical experiments using the network security laboratory-knowledge discovery and data mining (NSL-KDD) dataset to corroborate the results on the detection accuracy, security-privacy tradeoffs, and design.
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