Machine Learning for IoT Security: Advanced Threat Analysis Methods

Abstract

The rise of the Internet of Things (IoT) and Industrial IoT (IIoT) has introduced challenges in handling complex data and ensuring cybersecurity. This study explores how deep learning (DL) and active learning techniques can enhance the identification and classification of network security threats in IoT environments. Using the ToN IoT dataset which integrates diverse data from telemetry, operating systems, and network traffic the study applied exploratory data analysis (EDA) and advanced preprocessing to address data quality and imbalance issues. Active learning was incorporated into the machine learning pipeline to help models prioritize learning from the most informative data points. Experimental results demonstrated that ensemble models like Random Forest and Decision Tree, when combined with active learning, achieved high accuracy and showed strong potential for real-world deployment. In contrast, simpler models such as Logistic Regression were less effective in managing the data’s complexity. This research highlights the promise of integrating machine learning with adaptive learning approaches to improve cybersecurity defenses in IoT systems. The proposed framework contributes to the development of intelligent, evolving models that strengthen the cybersecurity capabilities of IoT and IIoT against specific cyber threats.