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Optimizing false positive in anomaly based intrusion detection using Genetic algorithm

机译：遗传算法优化基于基于异常的入侵检测的假阳性

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In recent years, with increasing use of internet the computer systems are facing many number of security issues. Intrusion detection system (IDS) is one of the principal components of any information security system. Identification of anomalous activity in computer network is first step in identifying the threat to information system. Our focus is mainly on Genetic algorithm (GA) based anomaly detection technique, as GA is one of the most effective evolutionary techniques for machine learning. In this paper Genetic algorithm is applied for network intrusion detection. Our approach for optimization specifically focusing on false positive rate. Reduction in false positive rate also improves accuracy and performance. The limitation of other techniques of accuracy, false positive rates has been addressed in this paper. Experimental results show the efficient detection rates based on KDD99cup datasets which is a standard dataset for intrusion detection.
机译：近年来，随着互联网使用的越来越多，计算机系统正面临许多安全问题。入侵检测系统（IDS）是任何信息安全系统的主要组件之一。识别计算机网络中的异常活动是识别信息系统威胁的第一步。我们的重点主要是基于遗传算法（GA）的异常检测技术，因为GA是机器学习最有效的进化技术之一。在本文中，遗传算法用于网络入侵检测。我们优化的方法专门关注误阳性率。减少假阳性率也提高了准确性和性能。在本文中已经解决了其他准确性技术的局限性，已经解决了假阳性率。实验结果显示了基于KDD99CUP数据集的有效检测率，该数据集是用于入侵检测的标准数据集。

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