Volume 16, Number 2

IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered Feature Selection with ML Model for IoT Threats & Attack Detection

  Authors

J. I. Christy Eunaicy¹, C. Jayapratha² and H.Salome Hemachitra³, ¹Thiagarajar College, India, ²Karpaga Vinayaga College of Engineering and Technology, India, ³Sri Meenakshi Government College of Arts for Women, India

  Abstract

Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.

  Keywords

Internet of Things (IoT) Guardian, Network Security, Intrusion Detection System (IDS), Anomaly Detection, Feature Selection, Game Theory, Isolation Forest, Local Outlier Factor (LOF), Support Vector Machine (SVM)