JCADS: Semi-Supervised Clustering Algorithm for Network Anomaly Intrusion Detection Systems

Detection of the anomaly activities in the network has been a growing problem, motivating widespread research in the area of automated intrusion detection systems. In the automated intrusion detection systems, classification of n-dimensional vectors of the network traffic is a challenging area. Several research works was already done on this topic. But most of the works were presented to have high detection rates, But with false positives. In this paper, we presented a novel approach to have a high detection rate and very low false positives and false negatives in the classification of network traffic using jaccords coefficient (JC) similarity. The proposed approach is employed on low dimensional space of network traffic profiles with the KDDCUP99 dataset. The experimental study shows that the use of jaccords coefficient similarity clustering on the network traffic profile will increases the detection rate and avoids the false positives in the classification.