DocumentCode
126922
Title
The effect of attribute pairings in intrusion detection
Author
Milliken, Michael ; Yaxin Bi ; Galway, L.
Author_Institution
Sch. of Comput. & Math., Univ. of Ulster, Newtownabbey, UK
fYear
2014
fDate
8-10 Sept. 2014
Firstpage
1
Lastpage
6
Abstract
As Network Intrusions have become larger and more pervasive the methods of detection have changed, a number of systems use ensemble methods to improve upon results from single classifiers or algorithms. The solutions proposed in the literature achieve good results, which primarily focus on classification of Network Intrusions by tailoring classification algorithms and feature selection. However fewer studies focus on investigation of relation between pairs of attributes, such as IP address and Port, as a single attribute. This paper proposes an effect analysis of pairs of attributes in order to improve intrusion detection using an ensemble-based classification approach.
Keywords
learning (artificial intelligence); security of data; attribute pairings; ensemble-based classification approach; feature selection; network intrusion detection; Algorithm design and analysis; Classification algorithms; Hidden Markov models; IP networks; Machine learning algorithms; Payloads; Ports (Computers); ensemble methods; intrusion detection; supervised machine learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence (UKCI), 2014 14th UK Workshop on
Conference_Location
Bradford
Type
conf
DOI
10.1109/UKCI.2014.6930185
Filename
6930185
Link To Document