Influence of Fractal Dimension on Network Anomalies Binary Classification Quality Using Machine Learning Methods

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.