Robust Distributed Intrusion Detection System for Edge of Things

The edge computing paradigm has been adopted in many Internet-of-Things (IoT) applications to improve responsiveness and conserve communication resources. However, such high agility and efficiency come with increased cyber threats. Intrusion detection systems (IDS) have been the primary means for guarding networked computing assets against hacking attempts. The popular design methodology for IDS relies on the application of machine learning (ML) techniques that use intelligence data to classify malicious activities. However, in the realm of IoT, insufficient data is available to build IDS; hence a distributed intrusion system with continual data collection is primordial to refine the detection model. Such IDS is also subject to privacy constraints and should sustain robustness against data manipulation from internal attackers that degrade the ML model. This paper opts to fulfill these requirements by proposing a novel distributed IDS for IoT. The proposed system employs federated learning to enable privacy preservation and diminish the communication overhead. Our system promotes a reinforcement mechanism to ensure resiliency to data manipulation attacks by single or colluding internal actors. The validation results using recently released datasets demonstrate the effectiveness of our approach.