International Conference on Mining Intelligence and Knowledge Exploration

Mining Intelligence and Knowledge Exploration pp 130-139

An Improved Intrusion Detection System Based on a Two Stage Alarm Correlation to Identify Outliers and False Alerts

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)

Abstract

To ensure the protection of computer networks from attacks, an intrusion detection system (IDS) should be included in the security architecture. Despite the detection of intrusions is the ultimate goal, IDSs generate a huge amount of false alerts which cannot be properly managed by the administrator, along with some noisy alerts or outliers. Many research works were conducted to improve IDS accuracy by reducing the rate of false alerts and eliminating outliers. In this paper, we propose a two-stage process to detect false alerts and outliers. In the first stage, we remove outliers from the set of meta-alerts using the best outliers detection method after evaluating the most cited ones in the literature. In the last stage, we propose a binary classification algorithm to classify meta-alerts whether as false alerts or real attacks. Experimental results show that our proposed process outperforms concurrent methods by considerably reducing the rate of false alerts and outliers.

Keywords

Clustering Binary classification False positives Intrusion detection systems Outliers 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.ISGUniversity of TunisTunisTunisia
  2. 2.University of TunisTunisia and Dhofar universitySalalahOman

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