A Semi-supervised Deep Auto-encoder Based Intrusion Detection for IoT

The main problem facing the Internet of Things (IoT) today is the identification of attacks due to the constrained nature of IoT devices. To address this problem, we present a lightweight intrusion detection system (IDS) which acts as a second line of defense allowing the reinforcement of the access control mechanism. The proposed method is based on a Deep Auto-Encoder (DAE), which learns the pattern of a normal process using only the features of the user’s normal behavior. Whatever deviation from the expected behavior is considered an anomaly. We validate our approach using two well-known network datasets, namely, the NSL-KDD and CIDDS-001. The experimental results demonstrate that our approach provides promising results in terms of accuracy, detection rate and false alarm rate.