A probability approach to anomaly detection with twin support vector machines |
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Authors: | Wei Nie Di He |
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Institution: | Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200240, China |
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Abstract: | Classification of intrusion attacks and normal network flow is a critical and challenging issue in network security study.
Many intelligent intrusion detection models are proposed, but their performances and efficiencies are not satisfied to real
computer networks. This paper presents a novel effective intrusion detection system based on statistic reference model and
twin support vector machines (TWSVMs). Moreover, a network flow feature selection procedure has been studied and implemented
with TWSVMs. The performances of proposed system are evaluated through using the fifth international conference on knowledge
discovery and data mining in 1999 (KDD’99) data set collected at MIT’s Lincoln Labs and the results indicate that the proposed
system is more efficient and effective than conventional support vector machines (SVMs) and TWSVMs. |
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