Abstract

In this paper, it is proposed to improve the efficiency of binary classification of network traffic anomalous behavior by introducing an additional informative feature – fractal dimension. The overall effectiveness of the proposed method is estimated by evaluating the quality of binary classification using the algorithms Decision Tree Classifier, Random Forest and Ada Boost on the example of using the NSL-KDD database. It is shown that adding the fractal dimension in the binary classification of attacks, gives improvement of the precision metric in average by 6%, and for AUC-ROC about 10% for all considered classification algorithms. Furthermore, introduction of fractal dimension as an additional feature has allowed to significantly reduce the time of training and testing of binary classification. So, for the Random Forest algorithm, the decrease in processing time was more than 3 times, and for the Decision Tree Classifier more than 2 times.

中文翻译：

---

分形维数对机器学习方法对网络异常二进制分类质量的影响

摘要

本文提出了通过引入附加的信息特征（分形维数）来提高网络流量异常行为的二进制分类效率。在使用NSL-KDD数据库的示例上，通过使用决策树分类器，随机森林和Ada Boost算法评估二进制分类的质量来评估该方法的总体有效性。结果表明，在攻击的二进制分类中增加分形维数，可以提高精度。指标平均降低6％，而对于所有考虑到的分类算法，AUC-ROC的平均值约为10％。此外，分形维数作为附加功能的引入已大大减少了对二进制分类的训练和测试时间。因此，对于随机森林算法，处理时间减少了3倍以上，而决策树分类器则减少了2倍以上。