Enhancing IoT Resilience: Machine Learning Techniques for Autonomous Anomaly Detection and Threat Mitigation

Abstract

The explosive growth of the Internet of Things (IoT) has had a substantial impact on daily life and businesses, allowing for real-time monitoring and decision-making. However, increased connectivity also brings higher security risks, such as botnet attacks and the need for stronger user authentication. This research explores how machine learning can enhance Internet of Things security by identifying abnormal activity, utilizing behavioral biometrics to secure cloud-based dashboards, and detecting botnet threats early. Researchers tested numerous machine learning methods, including K-Nearest Neighbors (KNN), Decision Trees, Logistic Regression, and XGBoost on publicly available datasets. The Decision Tree model earned an impressive accuracy rate of 0.73 for anomaly identification, proving its supremacy in dealing with complex security risks, while the XGBoost model demonstrated strong performance with a 92% accuracy rate for detecting TCP SYN flood attacks. Research findings show the effectiveness of these strategies in enhancing the security and reliability of IoT devices. This study provides significant insights into the use of machine learning to protect IoT devices while also addressing crucial concerns such as power consumption and privacy.