Intrusion detection system based on data mining techniques to reduce false alarm rate

Abstract EN

Nowadays, Security of network traffic is becoming a major issue of computer network system according to the huge development of internet.

Intrusion detection system has been used for discovering intrusion and to maintain the security information from attacks.

In this paper, produced two levels of mining algorithms to construct Network Intrusion Detection System (NIDS) and to reduce false alarm rate, in the first level Naïve Bayes algorithm is used to classify abnormal activity into the main four attack types from normal behavior.

In the second level ID3 decision tree algorithm is used to classify four attack types into (22) children of attacks from normal behavior.

To evaluate the performance of the two proposed algorithms by using kdd99 dataset intrusion detection system and the evaluation metric accuracy, precision, DR, F-measure.

The experimental results prove that the proposal system done high detection rates (DR) of 99 % and reduce false positives (FP) of 0 % for different types of network intrusions