Machine Learning Techniques for Network Intrusion Detection

DSpace/Manakin Repository

Search OPUS

Advanced Search


My Account

Show simple item record Tran, TP Tsai, PC Jan, T He, S
dc.contributor.editor Shawkat Ali, ABM
dc.contributor.editor Xiang, Y 2010-06-16T04:55:29Z 2010-01
dc.identifier.citation Dynamic and Advanced Data Mining for Progressing Technological Development, 2010, 1, pp. 273 - 299
dc.identifier.isbn 978-1-60566-908-3
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.
dc.publisher IGI Global
dc.title Machine Learning Techniques for Network Intrusion Detection
dc.type Chapter
dc.parent Dynamic and Advanced Data Mining for Progressing Technological Development
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 Image Processing
dc.for 080104 Computer Vision
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 990421
dc.personcode 020524
dc.personcode 044177
dc.personcode 999525
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
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computing and Communications
utslib.copyright.status Closed Access 2015-04-15 12:17:09.805752+10
utslib.collection.history School of Computing and Communications (ID: 335)
utslib.collection.history Closed (ID: 3)

Files in this item

This item appears in the following Collection(s)

Show simple item record