A Semi-supervised Deep Auto-encoder Based Intrusion Detection for IoT
Samir Fenanir, Fouzi Semchedine, Saad Harous, Abderrahmane Baadache · Ingénierie des systèmes d information · 2020
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.