A Lightweight 5G-V2X Intra-Slice Intrusion Detection System Using Knowledge Distillation

• Main Authors: Hossain, Shajjad, Boualouache, Abdelwahab, Brik, Bouziane, Senouci, Sidi-Mohammed
• Format: Conference Proceeding
• Language: English
• Subjects: 5G-V2X; Biological system modeling; Deep learning; IDS; Industries; Knowledge Distillation; Knowledge engineering; Network slicing; Privacy; Security; Training
• ISSN: 1938-1883
• DOI: 10.1109/ICC45041.2023.10279212

As the automotive industry grows, modern vehicles will be connected to 5G networks, creating a new Vehicular-to-Everything (V2X) ecosystem. Network Slicing (NS) supports this 5G-V2X ecosystem by enabling network operators to flexibly provide dedicated logical networks addressing use case specific-requirements on top of a shared physical infrastructure. Despite its benefits, NS is highly vulnerable to privacy and security threats, which can put Connected and Automated Vehicles (CAVs) in dangerous situations. Deep Learning-based Intrusion Detection Systems (DL-based IDSs) have been proposed as the first defense line to detect and report these attacks. However, current DL-based IDSs are processing and memory-consuming, increasing security costs and jeopardizing 5G-V2X acceptance. To this end, this paper proposes a lightweight intrusion detection scheme for 5G-V2X sliced networks. Our scheme leverages DL and Knowledge Distillation (KD) for training in the cloud and offloading knowledge to slice-tailored lightweight DL models running on CAVs. Our results show that our scheme provides an optimal trade-off between detection accuracy and security overhead. Specifically, it can reduce security overhead in computation and memory complexity to more than 50% while keeping almost the same performance as heavy DL-based IDSs.