Agent-oriented network intrusion detection system using data mining approaches

Document Type

Journal Article

Publication Date

2007

Keywords

Network intrusion detection, Agents, Data mining, Clustering, Association rules, Sequential association rules, Agent-oriented software, Network behaviour, Agent-based systems, Multi-agent systems

DOI

10.1504/IJAOSE.2007.014403

Abstract

Most of the existing commercial Network Intrusion Detection System (NIDS) products are signature-based but not adaptive. In this paper, an adaptive NIDS using data mining technology is developed. Data mining approaches are used to accurately capture the actual behaviour of network traffic, and the portfolio mined is useful for differentiating 'normal' and 'attack' traffics. On the other hand, most of the current researches use only one engine for detection of various attacks; the proposed system, which is constructed by a number of agents, is totally different in both training and detecting processes. Each of the agents has its own strength in capturing a kind of network behaviour and hence the system has strength in detecting different types of attack. In addition, its ability in detecting new types of attack and its higher tolerance to fluctuations were shown. The experimental results showed that the frequent patterns mined from the audit data could be used as reliable agents, which outperformed the traditional signature-based NIDS.

Source Publication

International Journal of Agent-Oriented Software Engineering

Volume Number

1

Issue Number

2

ISSN

1746-1375

First Page

158

Last Page

174

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