A hybrid artificial immune system and Self Organising Map for network intrusion detection

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Version: Author's accepted manuscript
Serval ID
serval:BIB_6702D8280CD7
Type
Article: article from journal or magazin.
Collection
Publications
Title
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Journal
Information Sciences
Author(s)
Powers S.T., He J.
Publication state
Published
Issued date
2008
Peer-reviewed
Oui
Volume
178
Number
15
Pages
3024-3042
Language
english
Abstract
Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer systems over a network. Two broad approaches exist to tackle this problem: anomaly detection and misuse detection. An anomaly detection system is trained only on examples of normal connections, and thus has the potential to detect novel attacks. However, many anomaly detection systems simply report the anomalous activity, rather than analysing it further in order to report higher-level information that is of more use to a security officer. On the other hand, misuse detection systems recognise known attack patterns, thereby allowing them to provide more detailed information about an intrusion. However, such systems cannot detect novel attacks.
A hybrid system is presented in this paper with the aim of combining the advantages of both approaches. Specifically, anomalous network connections are initially detected using an artificial immune system. Connections that are flagged as anomalous are then categorised using a Kohonen Self Organising Map, allowing higher-level information, in the form of cluster membership, to be extracted. Experimental results on the KDD 1999 Cup dataset show a low false positive rate and a detection and classification rate for Denial-of-Service and User-to-Root attacks that is higher than those in a sample of other works.
Create date
22/01/2012 20:27
Last modification date
20/08/2019 14:22
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