INTRUSION DETECTION BY BACKPROPAGATION NEURAL NETWORKS WITH SAMPLE-QUERY AND ATTRIBUTE-QUERY

The growing network intrusions have put companies and organizations at a much greater risk of loss. In this paper, we propose a new learning methodology towards developing a novel intrusion detection system (IDS) by backpropagation neural networks (BPN) with sample-query and attribute-query. We test the proposed method by a benchmark intrusion dataset to verify its feasibility and effectiveness. Results show that choosing good attributes and samples will not only have impact on the performance, but also on the overall execution efficiency. The proposed method can significantly reduce the training time required. Additionally, the training results are good. It provides a powerful tool to help supervisors analyze, model and understand the complex attack behavior of electronic crime.

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