INTRUSION DETECTION USING A MIXED FEATURES FUZZY CLUSTERING ALGORITHM - Hameed, Sarab (Iraqi Journal of Science, 2024)
Proliferation of network systems and growing usage of Internet make networksecurity issue to be more important. Intrusion detection is an important factor in keepingnetwork secure. The main aim of intrusion detection is to classify behavior of a systeminto normal and intrusive behaviors. However, the normal and the attack behaviors innetworks are hard to predict as the boundaries between them cannot be well distinct.This paper presents an algorithm for intrusion detection that combines both fuzzy CMeans (FCM) and FCM for symbolic features algorithms in one. Experimental resultson the Knowledge Discovery and Data Mining Cup 1999 (KDD cup 99) intrusiondetection dataset show that the average detection rate of this algorithm is 99%. Theresults indicate that the proposed algorithm is able to distinguish between normal andattack behaviors with high detection rate.