Classification of Darknet Traffic Using the AdaBoost Classifier Method

Abstract

Darknet is famous for its ability to provide anonymity which is often used for illegal activities. A security monitor report from BSSN highlights that 290.556 credential data from institution in Indonesia have been exposed on the darknet. Classification techniques are important for studying and identifying darknet traffic. This study proposes the utilization of the AdaBoost Classifier in darknet classification. The use of variable estimator values significantly impact classification results. The best performance was obtained with an estimator value of 500 with an accuracy of 99.70%. The contribution of this research lies in the development of classification models and the evaluation of AdaBoost models in the context of darknet traffic classification.