Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT)

Naveed Ahmed, Md Asri Ngadi, Abdulaleem Ali Almazroi, Nouf Atiahallah Alghanmi

To combat the growing danger of zero-day attacks on IoT networks, this study introduces a Cluster-Based Classification (CBC) method. Security vulnerabilities have become more apparent with the growth of IoT devices, calling for new approaches to identify unique threats quickly. The hybrid CBC approach uses optimized k-means clustering to find commonalities across different abnormalities, intending to quickly identify and classify unknown harmful attacks in a varied IoT network. The technique is fine-tuned for eight-class and two-class classifications, supporting different attacks using the IoTCIC2023 dataset and SelectKBest feature selection. Robust analysis is achieved by evaluating and aggregating the performance of machine learning classifiers such as XGBoost, AdaBoost, KNN, and Random Forest. In two-class classification, Random Forest achieves 95.11% accuracy, while in eight-class classification, KNN tops the charts with 88.24%. These results demonstrate noteworthy accuracy. The suggested CBC technique is effective, as shown by comparisons with state-of-the-art approaches. Despite several caveats and dataset specifications, this study provides a useful tool for academics and practitioners in the ever-changing field of cybersecurity by suggesting a method to strengthen the security of IoT networks against new threats.