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 :
بازگشت