Enhancing IoT Security: A Machine Learning Approach to Intrusion Detection System Evaluation

The advent of the Internet of Things (IoT) has revolutionised the way devices connect to each other, but it has also brought with it significant security concerns. Intrusion detection systems (IDS) have been deployed as a countermeasure against the increasing number of cyber attacks on IoT devices. Machine learning algorithms have shown promising capabilities in detecting these attacks. In this research paper, we present a comprehensive performance analysis of different machine learning algorithms used in IoT intrusion detection systems. Specifically, we evaluate six different algorithms: Logistic Regression, K-Nearest Neighbours, Decision Tree, Random Forest, Gradient Boosting and XGBoost. Our evaluation focuses on their effectiveness in detecting fraudulent activities in the IoT domain. Several evaluation metrics such as accuracy, precision, recall, F1 score and Matthews correlation coefficient are used to assess performance. This comprehensive analysis helps improve IoT security and provides valuable insights into the efficiency of machine learning algorithms for intrusion detection. Our experimental results highlight the outstanding performance of the Random Forest model on all evaluation metrics.