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首页> 外文期刊>Procedia Computer Science >Feature Analysis, Evaluation and Comparisons of Classification Algorithms Based on Noisy Intrusion Dataset
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Feature Analysis, Evaluation and Comparisons of Classification Algorithms Based on Noisy Intrusion Dataset

机译：基于噪声入侵数据集的分类算法的特征分析，评估与比较

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摘要

Various studies have been carried on an Intrusion Detection System (IDS) environment bycomparingthe performance of various Machine Learning (ML)based on a refined intrusion dataset with an error-free environment. However, the real-world network data deals with a large amount of noisy information on transmission, and the IDS have to work in such an environment frequently. Dealing with such noisy data is, therefore, a challenging issue in an IDS environment for detecting threads from network activities. In this paper, various Data Mining (DM) and ML algorithms are evaluated and compared by normal and noisy dataset prepared from KDD’99 and NSL-KDD dataset (10%-20% Noise). The empirical results demonstrate that NN (SOM) is far better compared to other tested algorithms regarding robustness tonoisy environment; however,JRip and J48 from the tree family outperform others regarding overall performance matrices. Feature dependency on datasets for a specific classifier is analyzedby Performance-based Method of Ranking (PMR). The evaluation results statistically proved that each classifier has a unique combination of a feature subset to results optimal performance. Empirical results demonstrate that evaluations of IDS based on NSL-KDD give more realistic results compared to theKDD’99 original dataset.
机译：通过基于精炼的入侵数据集和无错误环境比较各种机器学习（ML）的性能，对入侵检测系统（IDS）环境进行了各种研究。但是，现实世界的网络数据在传输时会处理大量的噪音信息，因此IDS必须经常在这种环境中工作。因此，在用于从网络活动中检测线程的IDS环境中，处理此类噪声数据是一个具有挑战性的问题。在本文中，通过从KDD’99和NSL-KDD数据集（噪声为10％-20％）准备的正常和有噪声的数据集，评估并比较了各种数据挖掘（DM）和ML算法。实验结果表明，与其他经过测试的算法相比，NN（SOM）在鲁棒性强的嘈杂环境中要好得多。但是，在总体性能矩阵方面，树家族的JRip和J48优于其他公司。通过基于性能的排名方法（PMR）分析特定分类器对数据集的特征依赖性。评估结果从统计学上证明了每个分类器具有特征子集的唯一组合以实现最佳性能。经验结果表明，与KDD’99原始数据集相比，基于NSL-KDD的IDS评估给出了更为现实的结果。

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