Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms

Abstract

The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the “SensorNetGuard” dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The “SensorNetGuard” dataset is available on the IEEE data port and Kaggle platform.