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Performance Analysis on Competitive, Roulette Wheel and Pseudo-Random Rules for Intrusion Detection |
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PP: 63-69 |
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Author(s) |
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Ruey-Maw Chen,
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Abstract |
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| Intrusion detection is a critical component of network security; detection schemes fundamentally use the observed
characteristics of network packets as a basis for such determinations. Meanwhile, intrusion detection can be regarded as a clustering
problem; many clustering schemes have been applied for classifying network packets. Among them, back propagation networks (BPN)
and fuzzy c-means (FCM) are popular and well applied. Both of these schemes are based on a competitive characteristic. Nevertheless,
a competitive characteristic may cause impropriate clustering results for intrusion detection. Hence, in this study, different clustering
criteria are proposed and adopted in BPN and FCM for classifying intrusion packet type; they are the roulette wheel selection rule
and pseudo-random rule. Moreover, KDDCUP99 data sets were used as the evaluation packet samples of the experiments, and the
given 41 packet features are reduced to 9, 11 and 24 key features for experimentation. Simulation results demonstrate that the proposed
intrusion detection criteria applied in BPN yields higher detection rates for the U2R and R2L connections; misclassification of U2R
and R2L connections would allow greater damage. Additionally, the suggested roulette wheel selection rule and pseudo-random rule
intrusion detection criteria integrated into BPN are superior to other schemes with only 11 features used further reducing complexity
and computation time. |
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