Context-Aware Adaptive Anomaly Detection in IoT Systems

The deployment of Internet-of-Things (IoT) devices in cyber-physical applications has introduced a new set of vulnerabilities. These security and reliability challenges require a holistic solution due to the cross-domain, cross-layer, and interdisciplinary nature of IoT systems. However, most works presented in the literature primarily focus on the cyber aspect, including the network and application layers, and the physical layer is overlooked. In this chapter, we utilize IoT sensors that capture the physical properties of the system to ensure the integrity of IoT sensor data and identify anomalous incidents in the environment. We propose an adaptive context-aware anomaly detection method optimized for fog computing. In this approach (Yasaei et al., IoT-CAD: context-aware adaptive anomaly detection in IoT systems through sensor association. In: 2020 IEEE/ACM International Conference on Computer Aided Design (ICCAD), pp. 1–9. IEEE, Piscataway (2020)) (Copyright Ⓒ2020 IEEE), we devise a novel sensor association algorithm that generates fingerprints of sensors, clusters them, and extracts the context of the system. Based on the contextual information, our predictor model, which comprises a long short-term memory (LSTM) neural network and Gaussian estimator, detects anomalies, and a consensus algorithm identifies the anomaly source. Furthermore, our model updates itself to adapt to the variation in the environment and system. The results demonstrate that our model detects the anomaly with 92.0% precision in 532ms, which meets the real-time constraint of the system under test.