Filtering intrusion detection alarms

Mansour, Nashat and Chehab, Maya I. and Faour, Ahmad (2010) Filtering intrusion detection alarms. Cluster Computing, 13 (1). pp. 19-29. ISSN 1386-7857

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Abstract

A Network Intrusion Detection System (NIDS) is an alarm system for networks. NIDS monitors all network actions and generates alarms when it detects suspicious or malicious attempts. A false positive alarm is generated when the NIDS misclassifies a normal action in the network as an attack.We present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by a NIDS. Our data mining technique is based on a Growing Hierarchical Self-Organizing Map (GHSOM) that adjusts its architecture during an unsupervised training process according to the characteristics of the input alarm data. GHSOM clusters these alarms in a way that supports network administrators in making decisions about true and false alarms. Our empirical results show that our technique is effective for real-world intrusion data.

Item Type:	Article
Additional Information:	Submitted to REF 2014, UoA 11, Maya Chehab
Identification Number:	10.1007/s10586-009-0096-9
Dates:	Date Event 2010 UNSPECIFIED
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Users 18 not found.
Date Deposited:	07 Jun 2016 12:33
Last Modified:	22 Mar 2023 12:02
URI:	https://www.open-access.bcu.ac.uk/id/eprint/248

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