Intrusion Detection System Based on Data Mining Techniques to Reduce False Alarm Rate

Authors

• Sarah Shareef, Computer Science Department, University of Technology, Baghdad, IraqFollow
• soukeana hashim, computer science departement\university of technologyFollow

Document Type

Research Paper

Abstract

Nowadays, Security of network traffic is becoming a major issue ofcomputer network system according to the huge development of internet.Intrusion detection system has been used for discovering intrusion and tomaintain the security information from attacks. In this paper, produced twolevels of mining algorithms to construct Network Intrusion Detection System(NIDS) and to reduce false alarm rate, in the first level Naïve Bayes algorithmis used to classify abnormal activity into the main four attack types fromnormal behavior. In the second level ID3 decision tree algorithm is used toclassify four attack types into (22) children of attacks from normal behavior.To evaluate the performance of the two proposed algorithms by using kdd99dataset intrusion detection system and the evaluation metric accuracy,precision, DR, F-measure. The experimental results prove that the proposalsystem done high detection rates (DR) of 99 % and reduce false positives (FP)of 0 % for different types of network intrusions.

Recommended Citation

Shareef, Sarah and hashim, soukeana (2026) "Intrusion Detection System Based on Data Mining Techniques to Reduce False Alarm Rate," Engineering and Technology Journal: Vol. 36: Iss. 2, Article 18.
DOI: https://doi.org/10.30684/etj.36.2B.3

DOI

10.30684/etj.36.2B.3

First Page

110

Last Page

119