Resource-Efficient Traffic Classification Using Feature Selection for Message Queuing Telemetry Transport-Internet of Things Network-Based Security Attacks

Abstract

The rapid proliferation of IoT devices necessitates robust security measures to protect against malicious traffic. Anomaly detection, primarily through traffic classification supported by artificial intelligence and machine learning techniques, has emerged as a practical approach to enhancing IoT network security. Effective traffic classification requires efficient feature selection, which is critical for resource-constrained IoT devices with limited computational power, memory, and energy. This study proposes Statistical Moments Difference Thresholding, a feature selection method leveraging statistical central moments to identify significant features distinguishing between legitimate and malicious traffic. The aim is to reduce feature dimensionality while maintaining high detection accuracy. Validated on the MQTTset dataset through binary and multiclass classification using seven ML algorithms, the results highlight its ability to enhance computational efficiency without compromising performance, showcasing its potential in real-world IoT security applications.