Network Intrusion Detection with Workflow Feature Definition Using BP Neural Network

Authors:
Yong Wang;Dawu Gu;Wei Li;Hongjiao Li;Jing Li
Affiliations:
Department of Computers Science and Enginerring, Shanghai Jiao Tong University, Shanghai, China 200240 and Department of Computers Science and Technolgy, Shanghai University of Electric Power, Sha ...;Department of Computers Science and Enginerring, Shanghai Jiao Tong University, Shanghai, China 200240;Department of Computers Science and Enginerring, Shanghai Jiao Tong University, Shanghai, China 200240;Department of Computers Science and Technolgy, Shanghai University of Electric Power, Shanghai, China 20090;Department of Computers Science and Technolgy, Shanghai University of Electric Power, Shanghai, China 20090
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

The major problem of existing intrusion detection using neural network models is recognition of new attacks and low accuracy. The paper describes an intrusion detection method based on workflow feature definition according to KDD cup 99 types with feed forward BP neural network. The workflow can define new attacks sequence to help BP neural network recognize new attacks. The method takes network traffic data to analyze and classify the behaviors of the authorized users and recognize the possible attacks. The experiment results show that the design is effective.