| Title | A machine learning based approach for the detection of sybil attacks in C-ITS |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Hammi, Badis, Idir, Mohamed Yacine, Khatoun, Rida |
| Conference Name | 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS) |
| Date Published | sep |
| Keywords | C-ITS, certificate, composability, intelligent transportation systems, Intrusion detection, machine learning, Metrics, PKI, privacy, pseudonym, pubcrawl, Resiliency, security, sustainable development, Sybil attack, sybil attacks, VANET |
| Abstract | The intrusion detection systems are vital for the sustainability of Cooperative Intelligent Transportation Systems (C-ITS) and the detection of sybil attacks are particularly challenging. In this work, we propose a novel approach for the detection of sybil attacks in C-ITS environments. We provide an evaluation of our approach using extensive simulations that rely on real traces, showing our detection approach's effectiveness. |
| DOI | 10.23919/APNOMS56106.2022.9919991 |
| Citation Key | hammi_machine_2022 |
A machine learning based approach for the detection of sybil attacks in C-ITS