A PCA-based Method for IoT Network Traffic Anomaly Detection

Anomaly detection in IoT (Internet of Things) networks is becoming a challenge task due to limited network resources and performance. Comprehensive detection methods are no longer effective for IoT networks, calling for developing lightweight solutions. Methods using Principal Component Analysis (PCA) is an attractive approach due to complexity reduction. However, there are remaining issues by applying PCA such as the choice of principal components for complexity reduction. This paper investigates PCA techniques used in previous typical research works and proposes a new general formula for distance calculation and a new detection method based on PCA for IoT networks. The paper investigates formula parameters for the new method and presented the quick detection of network traffic anomalies with lower complexity.