Leveraging Machine Learning for IoT Traffic Analysis: Enhancing Privacy and Detecting Malicious Behavior

Abstract

With businesses making more and more use of IoT devices, they are benefited with improved connectivity and simpler operations. But at the same time, this expansion in technology also brings along some critical data security and privacy risk. Pre-determined rule-based security measures might be inadequate in the face of adaptive cyber threats. For this, real-time traffic analysis of IoT, fueled by advanced machine learning (ML) technologies, is gaining more and more relevance to counter such threats. This work utilizes ML-based techniques to increase threat detection, identify malicious activities, and strengthen data security. Our approach consists of supervised and unsupervised learning models, utilizing Random Forest for intrusion detection and t-SNE with K-Means clustering for anomaly detection. The research utilizes the publicly available N-BaIoT dataset with careful feature engineering and standardization to achieve optimal performance. Our results demonstrate that the Random Forest model has an accuracy rate of 91% clustering methods efficiently distinguish between normal and malicious traffic. These outcomes indicate the potential of ML-based solutions to increase threat detection efficiency and minimize false positives compared to traditional approaches. Subsequent studies will explore real-time deployment, computational efficiency optimization, and compliance of AI models with regulations in order to offer effective IoT security. This study is part of the ongoing efforts to improve cyber threat defense mechanisms without infringing on user privacy in connected environments.