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An Ensemble-Based Scalable Approach for Intrusion Detection Using Big Data Framework
Big Data ( IF 4.6 ) Pub Date : 2021-08-16 , DOI: 10.1089/big.2020.0201
Santosh Kumar Sahu 1 , Durga Prasad Mohapatra 1 , Jitendra Kumar Rout 2 , Kshira Sagar Sahoo 3 , Ashish Kr Luhach 4
Affiliation  

In this study, we set up a scalable framework for large-scale data processing and analytics using the big data framework. The popular classification methods are implemented, tuned, and evaluated by using intrusion datasets. The objective is to select the best classifier after optimizing the hyper-parameters. We observed that the decision tree (DT) approach outperforms compared with other methods in terms of classification accuracy, fast training time, and improved average prediction rate. Therefore, it is selected as a base classifier in our proposed ensemble approach to study class imbalance. As the intrusion datasets are imbalanced, most of the classification techniques are biased toward the majority class. The misclassification rate is more in the case of the minority class. An ensemble-based method is proposed by using K-Means, RUSBoost, and DT approaches to mitigate the class imbalance problem; empirically investigate the impact of class imbalance on classification approaches' performance; and compare the result by using popular performance metrics such as Balanced Accuracy, Matthews Correlation Coefficient, and F-Measure, which are more suitable for the assessment of imbalanced datasets.

中文翻译:

使用大数据框架的基于集成的可扩展入侵检测方法

在本研究中,我们使用大数据框架为大规模数据处理和分析建立了一个可扩展的框架。流行的分类方法是通过使用入侵数据集来实现、调整和评估的。目标是在优化超参数后选择最佳分类器。我们观察到决策树 (DT) 方法在分类准确性、快速训练时间和提高的平均预测率方面优于其他方法。因此,在我们提出的集成方法中选择它作为基础分类器来研究类不平衡。由于入侵数据集不平衡,大多数分类技术偏向于多数类。在少数类的情况下,误分类率更高。提出了一种基于集成的方法,使用 K-Means、RUSBoost、和 DT 方法来缓解类不平衡问题;实证研究类不平衡对分类方法性能的影响;并使用更适合评估不平衡数据集的流行性能指标(例如 Balanced Accuracy、Matthews Correlation Coefficient 和 F-Measure)来比较结果。
更新日期:2021-08-17
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