Active-Darknet: An Iterative Learning Approach for Darknet Traffic Detection and Categorization

Abstract

Darknet refers to a significant portion of the internet that is hidden and not indexed by traditional search engines. It is often associated with illicit activities such as the trafficking of illicit goods, such as drugs, weapons, and stolen data. To keep our online cyber spaces safe in this era of rapid technological advancement and global connectivity, we should analyse and recognise darknet traffic. Beyond cybersecurity, this attention to detail includes safeguarding intellectual property, stopping illegal activity, and following the law. In order to improve accuracy and precision in identifying illicit activities, this study presents a novel approach named Active-Darknet that uses an active learning-based machine learning model for detecting darknet traffic. In order to guarantee high-quality analysis, our methodology includes extensive data preprocessing, such as numerically encoding categorical labels and improving the representation of minority classes using data balancing. In addition to machine learning models, we also use Deep Neural Networks (DNN), Bidirectional Long Short-Term Memory (BI-LSTM) and Flattened-DNN for experimentation. The majority of models exhibited encouraging outcomes; however, the models that utilised active learning, specifically the Random Forest (RF) and Decision Tree (DT) models, attained promising accuracy levels of 87%, rendering them the most efficient in detecting darknet traffic. Large traffic analysis is greatly enhanced by this method, which also increases the detection process’s robustness and effectiveness.

Keywords

• Active learning
• darknet
• anonymity
• encrypted networks
• encrypted traffic
• machine learning