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

Full text not available from this repository.

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
Subjects: G400 Computer Science
G500 Information Systems
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cyber Security
UoA Collections > UoA11: Computer Science and Informatics
Depositing User: Miss Jessica Baylis
Date Deposited: 07 Jun 2016 12:33
Last Modified: 07 Jun 2016 12:33
URI: http://www.open-access.bcu.ac.uk/id/eprint/248

Actions (login required)

View Item View Item

Research

In this section...