提高基于混合技术的入侵检测率

2013 5th Computer Science and Electronic Engineering Conference (CEEC) Pub Date : 2013-11-11 DOI:10.1109/CEEC.2013.6659468

Khattab M. Ali Alheeti, L. Al-Jobouri, K. Mcdonald-Maier

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引用次数: 6

摘要

本文提出了提高入侵检测率的技术。这些技术是基于被检测到的特定特征，并且已经证明，与大量特征相比，少量特征(9)可以产生更高的检测率。这些技术利用软计算技术，如基于反向传播的人工神经网络和模糊集。这些技术实现了对标准DARPA基准数据的技术状态的显著改进。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Increasing the rate of intrusion detection based on a hybrid technique

This paper presents techniques to increase intrusion detection rates. Theses techniques are based on specific features that are detected and it's shown that a small number of features (9) can yield improved detection rates compared to higher numbers. These techniques utilize soft computing techniques such a Backpropagation based artificial neural networks and fuzzy sets. These techniques achieve a significant improvement over the state of the art for standard DARPA benchmark data.

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2013 5th Computer Science and Electronic Engineering Conference (CEEC)

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