Proliferation of network systems and growing usage of Internet make network
security issue to be more important. Intrusion detection is an important factor in keeping
network secure. The main aim of intrusion detection is to classify behavior of a system
into normal and intrusive behaviors. However, the normal and the attack behaviors in
networks 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 C
Means (FCM) and FCM for symbolic features algorithms in one. Experimental results
on the Knowledge Discovery and Data Mining Cup 1999 (KDD cup 99) intrusion
detection dataset show that the average detection rate of this algorithm is 99%. The
results indicate that the proposed algorithm is able to distinguish between normal and
attack behaviors with high detection rate.
INTRUSION DETECTION USING A MIXED FEATURES FUZZY CLUSTERING ALGORITHM