Machine Learning Techniques for Network Intrusion Detection

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Show simple item record Tran, Tich en_US Tsai, Po-Hsiang en_US Jan, Tony en_US He, Sean en_US
dc.contributor.editor Shawkat Ali, A.B.M. & Xiang, Y. en_US 2010-06-16T04:55:29Z 2010-06-16T04:55:29Z 2010 en_US
dc.identifier 2009001945 en_US
dc.identifier.citation Tran Tich Phuoc et al. 2010, 'Machine Learning Techniques for Network Intrusion Detection', in NA (ed.), IGI Global, New York, USA, pp. 273-299. en_US
dc.identifier.issn 978-1-60566-908-3 en_US
dc.identifier.other B1 en_US
dc.description.abstract Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of cyber attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool is imperative for computer networks. Alongside the existing prevention techniques such as encryption and firewalls, Intrusion Detection System (IDS) has established itself as an emerging technology that is able to detect unauthorized access and abuse of computer systems by both internal users and external offenders. Most of the novel approaches in this field have adopted Artificial Intelligence (AI) technologies such as Artificial Neural Networks (ANN) to improve performance as well as robustness of IDS. The true power and advantages of ANN lie in its ability to represent both linear and non-linear relationships and learn these relationships directly from the data being modeled. However, ANN is computationally expensive due to its demanding processing power and this leads to overfitting problem, i.e. the network is unable to extrapolate accurately once the input is outside of the training data range. These limitations challenge IDS with low detection rate, high false alarm rate and excessive computation cost. This chapter proposes a novel Machine Learning (ML) algorithm to alleviate those difficulties of existing AI techniques in the area of computer network security. The Intrusion Detection dataset provided by Knowledge Discovery and Data Mining (KDD-99) is used as a benchmark to compare our model with other existing techniques. Extensive empirical analysis suggests that the proposed method outperforms other state-of-the-art learning algorithms in terms of learning bias, generalization variance and computational cost. It is also reported to significantly improve the overall detection capability for difficult-to-detect novel attacks which are unseen or irregularly occur in the training phase. en_US
dc.language English en_US
dc.publisher IGI Global en_US
dc.relation.isbasedon NA en_US
dc.title Machine Learning Techniques for Network Intrusion Detection en_US
dc.parent Dynamic and Advanced Data Mining for Progressing Technological Development en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation New York, USA en_US
dc.identifier.startpage 273 en_US
dc.identifier.endpage 299 en_US FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080106 en_US
dc.personcode 999525 en_US
dc.personcode 044177 en_US
dc.personcode 020524 en_US
dc.personcode 990421 en_US
dc.percentage 40 en_US Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords Network intrusion detection, Neural Network, Adaptive Boosting en_US
dc.staffid en_US
dc.staffid 990421 en_US

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