DocumentCode :
494905
Title :
Fuzzy mega cluster based anomaly network intrusion detection
Author :
Hubballi, Neminath ; Biswas, Santosh ; Nandi, Sukumar
Author_Institution :
Dept. of Comput. Sci. & Eng., IIT Guwahati, Guwahati, India
fYear :
2009
fDate :
24-26 June 2009
Firstpage :
1
Lastpage :
5
Abstract :
Most of the anomaly based techniques produce vast number of alert messages that include a large percentage of false alarms. One of the widely used technique for anomaly intrusion detection systems (IDS) is cluster analysis. In cluster based IDS, feature vectors generated from network traffic are grouped into clusters as normal or abnormal (raising alert). The main cause for false alert generation is either, technique fails to differentiate an outlier from a genuine cluster point or the features extracted fail to separate the two classes. In this work, fuzzy clustering technique for anomaly intrusion detection has been explored to reduce the false alarms. A technique to robustify the existing fuzzy c-means algorithm is proposed and subsequently used as anomaly IDS.
Keywords :
fuzzy set theory; pattern clustering; security of data; statistical analysis; anomaly network intrusion detection; cluster analysis; feature vector; fuzzy c-means algorithm; fuzzy clustering technique; fuzzy mega cluster; network traffic; Algorithm design and analysis; Clustering algorithms; Computer science; Feature extraction; Fuzzy systems; Intrusion detection; Prototypes; Robustness; Telecommunication traffic; Traffic control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Network and Service Security, 2009. N2S '09. International Conference on
Conference_Location :
Paris
Print_ISBN :
978-2-9532-4431-1
Type :
conf
Filename :
5161662
Link To Document :
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