Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach

Jiangang Shu

doi:10.1109/TITS.2020.3027390

Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach

2021-07-01

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DOI：10.1109/TITS.2020.3027390

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Vehicular Ad hoc Network (VANET) is an enabling technology to provide a variety of convenient services in intelligent transportation systems, and yet vulnerable to various intrusion attacks. Intrusion detection systems (IDSs) can mitigate the security threats by detecting abnormal network behaviours. However, existing IDS solutions are limited to detect abnormal network behaviors under local sub-networks rather than the entire VANET. To address this problem, we utilize deep learning with generative adversarial networks and explore distributed SDN to design a collaborative intrusion detection system (CIDS) for VANETs, which enables multiple SDN controllers jointly train a global intrusion detection model for the entire network without directly exchanging their sub-network flows. We prove the correctness of our CIDS in both IID (Independent Identically Distribution) and non-IID situations, and also evaluate its performance through both theoretical analysis and experimental evaluation on a real-world dataset. Detailed experimental results validate that our CIDS is efficient and effective in intrusion detection for VANETs.

Keywords:

Collaborative intrusion detection

intelligent transportation

distributed SDN

deep learning

generative adversarial networks

Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach

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