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首页> 外文期刊>International journal of natural computing research >Analysis of Feature Selection and Ensemble Classifier Methods for Intrusion Detection
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Analysis of Feature Selection and Ensemble Classifier Methods for Intrusion Detection

机译:入侵检测的特征选择和集合分类器方法分析

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摘要

Day by day network security is becoming more challenging task. Intrusion detection systems (IDSs) are one of the methods used to monitor the network activities. Data mining algorithms play a major role in the field of IDS. NSL-KDD'99 dataset is used to study the network traffic pattern which helps us to identify possible attacks takes place on the network. The dataset contains 41 attributes and one class attribute categorized as normal, DoS, Probe, R2L and U2R. In proposed methodology, it is necessary to reduce the false positive rate and improve the detection rate by reducing the dimensionality of the dataset, use of all 41 attributes in detection technology is not good practices. Four different feature selection methods like Chi-Square, SU, Gain Ratio and Information Gain feature are used to evaluate the attributes and unimportant features are removed to reduce the dimension of the data. Ensemble classification techniques like Boosting, Bagging, Stacking and Voting are used to observe the detection rate separately with three base algorithms called Decision stump, J48 and Random forest.
机译:网络安全日益成为一项更具挑战性的任务。入侵检测系统(IDS)是用于监视网络活动的方法之一。数据挖掘算法在IDS领域中起着重要作用。 NSL-KDD'99数据集用于研究网络流量模式,这有助于我们识别网络上可能发生的攻击。数据集包含41个属性和一类属性,分为正常,DoS,Probe,R2L和U2R。在所提出的方法中,有必要通过减小数据集的维数来减少误报率并提高检测率,在检测技术中使用所有41个属性不是一个好习惯。使用四种不同的特征选择方法(例如,Chi-Square,SU,增益比和信息增益特征)来评估属性,并删除不重要的特征以减少数据量。诸如Boosting,Bagging,Stacking和Voting之类的集成分类技术可通过称为决策树桩,J48和随机森林的三种基本算法分别观察检测率。

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