Artificial Intelligence

    

Anomalous Payload Detection System by the Combination of Sparse-Response Deep Belief Network and Support Vector Machine

Authors: Han Ok Chol, Hyon Hui Song, Pak Chol Ryong

This paper proposes how to detect malicious network data effectivelyby the combination of sparse-response deep belief network and support vector machine.The Sparse-response Deep belief networks (SR-DBN) is an efficient non-supervised leaning machine for learning feature representation of the data without redundancy and the Support Vector Machine is designed to develop a classifier, which has high generalization ability in the feature space, in a supervised manner. In this paper, the feature representation of anomalous payload is performed by Sparse-response Deep belief Networks(SR-DBN), while the classification of normal or abnormal payload is performed by Support Vector Machine. Simulations and experiments show that the proposed abnormal network-detecting system is higher detection rate than the multi-layer perceptron which has stacked auto-encoder.

Comments: 9 Pages.

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Submission history

[v1] 2023-09-22 00:36:36

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