Witcha ChimphleeMohd Noor Md SapAbdul Hanan AbdullahSiriporn ChimphleeSurat Srinoy2025-03-102025-03-102006Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 20062-s2.0-38049121550https://repository.dusit.ac.th//handle/123456789/5070The Traditional intrusion detection systems (IDS) look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. However, normal operation often produces traffic that matches likely "attack signature", resulting in false alarms. In this paper we propose an intrusion detection method that proposes rough set based feature selection heuristics and using fuzzy c-means for clustering data. Rough set has to decrease the amount of data and get rid of redundancy. Fuzzy Clustering methods allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to increase accuracy detection rate for suspicious activity and signature detection. Empirical studies using the network security data set from the DARPA 1998 offline intrusion detection project (KDD 1999 Cup) show the feasibility of misuse and anomaly detection results.Anomaly detectionFuzzy c-meansIntrusion detectionNetwork securityRough setSuspicious activityConference paperScopus