| Title | SDN-based Misbehavior Detection System for Vehicular Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Boualouache, A., Soua, R., Engel, T. |
| Conference Name | 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) |
| Date Published | may |
| Keywords | composability, computer network security, context-aware MDS, cryptography, cryptography solutions, data privacy, internal attacks, Metrics, Misbehaving Detection Systems, misbehavior detection systems, Monitoring, privacy, pubcrawl, Resiliency, SDN-based misbehavior Detection system, security, Software, software defined networking, Software Defined Networks, Software-Defined Networking paradigm, Standards, Sybil attack-resistant, sybil attacks, vehicular ad hoc networks, Vehicular Networks, vehicular privacy standards, Voting |
| Abstract | Vehicular networks are vulnerable to a variety of internal attacks. Misbehavior Detection Systems (MDS) are preferred over the cryptography solutions to detect such attacks. However, the existing misbehavior detection systems are static and do not adapt to the context of vehicles. To this end, we exploit the Software-Defined Networking (SDN) paradigm to propose a context-aware MDS. Based on the context, our proposed system can tune security parameters to provide accurate detection with low false positives. Our system is Sybil attack-resistant and compliant with vehicular privacy standards. The simulation results show that, under different contexts, our system provides a high detection ratio and low false positives compared to a static MDS. |
| DOI | 10.1109/VTC2020-Spring48590.2020.9128604 |
| Citation Key | boualouache_sdn-based_2020 |
SDN-based Misbehavior Detection System for Vehicular Networks