Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks
This addresses security and privacy concerns for connected and automated vehicles, but it is incremental as it builds on existing federated learning and auto-encoder methods.
The paper tackled the problem of detecting zero-day attacks in 5G and beyond V2X networks by proposing a federated learning-based intrusion detection system that uses deep auto-encoders on benign traffic patterns, achieving a high detection rate with minimized false positives and delay.
Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay.