A Comparative Evaluation of Machine Learning Algorithms for IDS in IoT network

With the increasing Internet use, network security has become essential due to the rise in cyber-attacks on network services. To detect these attacks, a robust Intrusion Detection System (IDS) is required. Traditional IDS face challenges like high false alert rates and slow real-time attack detection. Machine learning (ML) can improve this situation, providing a low False Alarm Rate and high detection rates. This research used five ML methods (Logistic Regression, Random Forest, k-Nearest Neighbors, Support Vector Machine, and XGBoost) to classify the UNSW-NB15 dataset. The goal is to evaluate the performance of various machine learning classifiers in detecting attacks for Internet of Things (IoT) network intrusion detection. The study highlighted the importance of further research to reduce false positives and negatives. To evaluate these classifiers, precision, accuracy, recall, and F1 score were used. The results show that XGBoost achieved the highest accuracy and recall. However, only some algorithms performed perfectly in all aspects, suggesting the need for diverse detection strategies. Future research should focus on developing comprehensive systems and ensemble approaches to minimize false alerts and missed detections.