Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 22, Issue 7, Pages 4519-4530Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3027390
Keywords
Collaborative intrusion detection; intelligent transportation; distributed SDN; deep learning; generative adversarial networks
Categories
Funding
- Key-Area Research and Development Program of Guangdong Province [2019B010136001]
- Natural Science Foundation of China [61732022, 61672195]
- Peng Cheng Laboratory Project of Guangdong Province [PCL2018KP004, PCL2018KP005]
Ask authors/readers for more resources
Vehicular Ad hoc Network (VANET) is vulnerable to intrusion attacks, and a collaborative intrusion detection system (CIDS) utilizing deep learning and generative adversarial networks has been designed to address this issue. The CIDS enables global monitoring of abnormal network behaviors in the entire VANET.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available