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Information theoretic feature extraction to reduce dimensionality of Genetic Network Programming based intrusion detection model

机译:信息理论特征提取减少基于遗传网络编程的入侵检测模型的维数

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Intrusion detection techniques require examining high volume of audit records so it is always challenging to extract minimal set of features to reduce dimensionality of the problem while maintaining efficient performance. Previous researchers analyzed Genetic Network Programming framework using all 41 features of KDD cup 99 dataset and found the efficiency of more than 90% at the cost of high dimensionality. We are proposing a new technique for the same framework with low dimensionality using information theoretic approach to select minimal set of features resulting in six attributes and giving the accuracy very close to their result. Feature selection is based on the hypothesis that all features are not at same relevance level with specific class. Simulation results with KDD cup 99 dataset indicates that our solution is giving accurate results as well as minimizing additional overheads.
机译:入侵检测技术需要检查大量的审计记录,因此提取最小的功能集以降低问题的维数并同时保持高效的性能始终是一项挑战。以前的研究人员使用KDD cup 99数据集的所有41个功能分析了遗传网络编程框架,发现以高维为代价的效率超过90%。我们正在使用信息理论方法针对具有低维度的同一框架提出一种新技术,以选择导致6个属性的最小特征集,并使其准确性非常接近其结果。特征选择基于以下假设:所有特征与特定类别的关联度都不相同。使用KDD cup 99数据集进行的仿真结果表明,我们的解决方案在提供准确结果的同时,还使附加费用降至最低。

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