Comparative Analysis of Machine Learning Techniques for Intrusion Detection in IoT Networks

The rapid increase of Internet of Things (IoT) devices has introduced significant security challenges, requiring the development of effective Intrusion Detection Systems (IDS) to protect networks from malicious attacks. This study presents a comparative analysis of five machine learning (ML) algorithms (Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), XGBoost, and Support Vector Machine (SVM)) for IoT intrusion detection using the NSL-KDD dataset. Linear Discriminant Analysis (LDA) is used as a feature extraction technique to optimize model performance by reducing data dimensionality while retaining critical information. Three LDA scenarios with 2, 3, and 4 extracted features are used to compare the mentioned ML algorithms using the performance metrics like accuracy, precision, recall, F1-score, and execution time. The results show that RF achieved the highest accuracy (98.76%) with a slightly higher execution times making it ideal for applications prioritizing accuracy. KNN and XGBoost displayed a balance between high accuracy and computational efficiency, with execution times suitable for real-time IoT applications, with KNN achieving the shortest execution time. The results also highlight the importance of selecting ML algorithms based on the trade-offs between accuracy and efficiency for IoT intrusion detection.