Hybrid intelligent feature selector framework for darknet traffic classification

With the rapid growth in the internet traffic, analysis and exploration of traffic classification has become more challenging task especially for dark net or dark web. Darknet, within the confines of deep web, has been witnessing illegitimate doings such as drug trafficking, terrorism, betting etc. Hence classification of darknet traffic is an important task. This paper presents intelligent framework for the darknet traffic categorization with proposed hybrid feature selector. The darknet traffic consists of data packet related information as input predictors for the model. Twenty-one machine learning models with and without feature selectors are presented to categorize the darknet traffic. We propose a Hybrid LASSO-Random Forest (HLRF) feature selector to reduce feature dimensionality. Classification of darknet traffic is evaluated with well-known KNN, Extra Tree and XGBoost classifiers. The performance of proposed models was assessed in terms of Accuracy, Precision, Recall, Harmonic Mean Value of True Positive Value (TPV) and Positive Predicted Value (PPV), Matthews Corelation Coefficients (MCC) and Jaccard Score. The experimental results approve that XGBoost with proposed HLRF feature selector outperforms in categorization of darknet traffic. The results revel that proposed XGBoost-HLRF model obtain an accuracy and recall value of 98.10% with precision of 98.12%. Comparison of XGBoost-HLRF model with other proposed state of art models are presented for performance assessment of our model.

Automated summary

This study proposes an intelligent framework for the categorization of darknet traffic, using a hybrid feature selector. The experimental results demonstrate that the proposed XGBoost-HLRF model performs well in the classification of darknet traffic.