RapidMiner-based Clustering Techniques for Enhancing Intrusion Detection System (IDS) Performance

Abstract

Cybersecurity is the process of protecting networks, computers, servers, mobile devices, electronic systems, and data against hostile intrusions. It is the need of hour to be protected from the latest cyber-attacks. By examining traffic, Intrusion Detection Systems (IDS) assists in identifying possible dangers, unauthorized access, and unusual activity and notifies administrators to take appropriate action. Machine Learning (ML) clustering techniques are being used widely to make IDS better. In this research study, by utilizing clustering and classification techniques, such as Support Vector Machines (SVM), Boosting Naïve Bayes (BNB), K-Mean, and K-Medoids, the efficiency of the clustering techniques is examined. Further, we divided our research study in to cyber-attacks prediction and cyber-attacks detection categories. We used SVM and BNB clustering approaches for cyber-attacks prediction and compared the results. K-Mean and K-Medoids clustering approaches are used for cyber-attacks detection and the results are compared. Finally, we concluded that SVM is better approach for cyber-attacks prediction and K-Medoid is better approach for cyber-attacks detection.