FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning
Date:
This talk presents FeCo, a machine-learning-based IDS for IoT networks. FeCo incorporates contrastive learning into FL framework to support distributed intrusion detection. FeCo obtains more representative feature vectors by contrastive learning. These feature vectors have higher discriminative power between normal and malicious traffic. This effectively enables FeCo to achieve better detection accuracy than other baselines. Through extensive evaluations on the NSL-KDD dataset, we demonstrate the high effectiveness of FeCo in both centralized and federated learning setting.