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Intrusion Detection by Backpropagation Neural Networks With Sample-Query and Attribute-Query

Tags:
network security

Overview The growing network intrusions have put companies and organizations at a much greater risk of loss. This paper proposes a new learning methodology towards developing a novel Intrusion Detection System (IDS) by BackPropagation Neural networks (BPN) with sample-query and attribute-query. The proposed method is tested 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.

Further White Paper Details
Publisher	Research India Publications	File Format	PDF
Date Published	November 2006
Format	White Papers
Topics	Network Security, Security Tools, Intrusion Detection Systems

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