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Design & analysis of clustering based intrusion detection schemes for e-governance
Muttoo, Sunil K.
Pal, Saibal K.
1.08 - Published Scientific Conference Contribution
UNG - University of Nova Gorica
The problem of attacks on various networks and information systems is increasing. And with systems working in public domain like those involved under E-Governance are facing more problems than others. So there is a need to work on either designing an altogether different intrusion detection system or improvement of the existing schemes with better optimization techniques and easy experimental setup. The current study discusses the design of an Intrusion Detection Scheme based on traditional clustering schemes like K-Means and Fuzzy C-Means along with Meta-heuristic scheme like Particle Swarm Optimization. The experimental setup includes comparative analysis of these schemes based on a different metric called Classification Ratio and traditional metric like Detection Rate. The experiment is conducted on a regular Kyoto Data Set used by many researchers in past, however the features extracted from this data are selected based on their relevance to the E-Governance system. The results shows a better and higher classification ratio for the Fuzzy based clustering in conjunction with meta-heuristic schemes. The development and simulations are carried out using MATLAB.
particle swarm optimization
intrusion detection system
network intrusion detection
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Record is a part of a monograph
Intelligent Systems Technologies and Applications 2016
Advances in intelligent systems and computing (Internet)
Place of publishing:
Juan Manuel Corchado
Design and analysis of clustering based intrusion detection schemes for e-governance