IoT-CAD: Context-Aware Adaptive Anomaly Detection in IoT Systems Through Sensor Association

The deployment of Internet of Things (IoT) devices in cyber-physical applications has introduced a new set of vulnerabilities. The new security and reliability challenges require a holistic solution due to the cross-domain, cross-layer, and interdisciplinary nature of IoT systems. However, the majority of works presented in the literature primarily focus on the cyber aspect, including the network and application layers, and the physical layer is often overlooked. In this paper, we utilize IoT sensors that capture the physical properties of the system to ensure the integrity of IoT sensors data and identify anomalous incidents in the environment. We propose an adaptive context-aware anomaly detection method that is optimized to run on a fog computing platform. In this approach, 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 an Long-Short Term Memory (LSTM) neural network and Gaussian estimator, detects anomalies, and a consensus algorithm identifies the source of the anomaly. 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.