Unraveling IoT Traffic Patterns: Leveraging Principal Component Analysis for Network Anomaly Detection and Optimization

This paper presents a novel approach to analyzing IoT traffic patterns using Principal Component Analysis (PCA) on traffic captures from multiple IoT devices. The increasing adoption of IoT devices has led to growing concerns about their security and the need for efficient methods to detect anomalies in network traffic. In this study, we employ PCA to identify key patterns and reduce the dimensionality of the IoT traffic data, enabling a more streamlined and efficient analysis. The advantages of using PCA in this context include its capability to identify hidden correlations in traffic data, reduce noise, and improve the detection of anomalies. By applying PCA to IoT traffic data, we demonstrate its effectiveness in detecting unusual traffic patterns and potential security threats. This research contributes to the development of advanced edge-based detection mechanisms, which can be crucial for securing IoT devices and networks. As edge computing becomes more prevalent, the ability to detect and mitigate risks at the network periphery is essential for maintaining the overall security of IoT ecosystems. Our findings highlight the potential of PCA as a valuable technique for enhancing edge-based detection methods, thereby contributing to a more robust and secure environment for IoT devices and their users.