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Automatic Feature Selection and Ensemble Classifier for Intrusion Detection
Journal of Physics: Conference Series Pub Date : 2021-04-08 , DOI: 10.1088/1742-6596/1856/1/012067
Changjian Lin 1 , Aiping Li 1 , Rong Jiang 1
Affiliation  

Anomaly-based Intrusion Detection System (ADS) is one of the technologies widely used in network topology. Although many supervised and unsupervised learning methods in the field of machine learning have been used to improve the efficiency of ADS, achieving good performance is still a challenging problem for existing intrusion detection algorithms. Firstly, there are few public datasets available for evaluation. Secondly, a single classifier may not perform well in detecting each type of attack. Third, some of the existing schemes focus on feature subset selection, while ignoring the design of the classification decision algorithm, or focus on the classification decision algorithm. In order to address this issue, a new intrusion detection framework is proposed by comparing and studying various feature selection technologies and classification decision algorithms in this paper. An automatic parameter adjustment scheme is designed for feature selection and ensemble classification. It avoids the need to obtain the optimal parameters through manual experiments in advance, and can improve the robustness of the parameters and the model. We use the most classic NSL-KDD dataset and the latest CICIDS2018 dataset for comparative experiments. The experimental results demonstrate its efficiency in terms of Accuracy and False Positive Rate.



中文翻译:

用于入侵检测的自动特征选择和集成分类器

基于异常的入侵检测系统(ADS)是网络拓扑中广泛使用的技术之一。尽管机器学习领域的许多监督和非监督学习方法已被用于提高 ADS 的效率,但对于现有的入侵检测算法来说,实现良好的性能仍然是一个具有挑战性的问题。首先,可用于评估的公共数据集很少。其次,单个分类器可能无法很好地检测每种类型的攻击。第三,现有的一些方案侧重于特征子集的选择,而忽略了分类决策算法的设计,或者侧重于分类决策算法。为了解决这个问题,本文通过比较和研究各种特征选择技术和分类决策算法,提出了一种新的入侵检测框架。为特征选择和集成分类设计了一种自动参数调整方案。避免了提前通过人工实验获得最优参数,可以提高参数和模型的鲁棒性。我们使用最经典的 NSL-KDD 数据集和最新的 CICIDS2018 数据集进行对比实验。实验结果证明了它在准确率和误报率方面的效率。并且可以提高参数和模型的鲁棒性。我们使用最经典的 NSL-KDD 数据集和最新的 CICIDS2018 数据集进行对比实验。实验结果证明了它在准确率和误报率方面的效率。并且可以提高参数和模型的鲁棒性。我们使用最经典的 NSL-KDD 数据集和最新的 CICIDS2018 数据集进行对比实验。实验结果证明了它在准确率和误报率方面的效率。

更新日期:2021-04-08
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