Developing a Cloud Intrusion Detection System with Filter-Based Features Selection Techniques and SVM Classifier

The rising usage of the cloud nowadays and its usage in various domains have made it more essential for all, which has led to an expansion in the size of data kept in the cloud. Data is the gold of our era; thus, it is important to protect it against any attacks. The intrusion detection system IDS is considered among of the most important solutions that address security issues and threats in the different models of cloud service delivery. IDS-based on machine learning (ML) has been developed to monitor and analyse data packets to detect abnormal behaviours and new attacks. The datasets utilized for these objectives are generally vast and include a lot of features, making computing very time-consuming. It is crucial to pick relevant features to include in the model, which produce better results and require less computation time than using all of the features. In this paper, we developed a system that combines filter-based feature selection with the support-vector-machine (SVM) model as a classifier. The NSL-KDD, Kyoto, and the CSE-CIC-IDS-2018 datasets are used to validate our system. We have compared with many existing methodologies and found that our proposed system outperformed the others in terms of accuracy, recall, precision, F-measure, and false-alarm rate.