A Comprehensive Analysis of the Machine Learning Algorithms in IoT IDS Systems

The Internet of Things (IoT) has revolutionized various industries by enabling seamless connectivity and communication between devices. However, this proliferation of interconnected devices has also introduced significant security challenges, particularly in terms of detecting and mitigating intrusion attempts. In this context, Intrusion Detection Systems (IDS) have become crucial for safeguarding IoT networks. This project presents a comprehensive analysis of various machine learning algorithms applied to IoT IDS systems using a publicly available dataset from Kaggle. The primary objective of this study is to evaluate the performance of different machine learning algorithms, namely Support Vector Machine (SVM), Decision Tree, Random Forest, Gaussian Naive Bayes, AdaBoost, XGBoost, and Logistic Regression, in detecting intrusions in IoT networks. The dataset used in this analysis comprises a variety of features extracted from IoT network traffic, providing a robust foundation for training and testing the algorithms. Each algorithm is meticulously implemented and its performance is assessed using standard metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). The comparative analysis highlights the strengths and weaknesses of each algorithm in the context of IoT IDS, offering insights into their applicability and effectiveness in real-world scenarios. The findings of this project contribute to the ongoing efforts to enhance the security of IoT networks by identifying the most suitable machine learning techniques for intrusion detection. By leveraging these insights, practitioners and researchers can develop more robust and efficient IDS systems, ultimately fortifying IoT ecosystems against emerging threats.

Keywords: Anomaly Detection, IoT Security, Adaptive Machine Learning, Threat Detection, Comparative Study.