The Darknet is a section of the internet that is encrypted and untraceable, making it a popular location for illicit and illegal activities. However, the anonymity and encryption provided by the network also make identifying and classifying network traffic significantly more difficult. The objective of this study was to provide a comprehensive review of the latest advancements in methods used for classifying darknet network traffic. The authors explored various techniques and methods used to classify traffic, along with the challenges and limitations faced by researchers and practitioners in this field. The study found that current methods for traffic classification in the Darknet have an average classification error rate of around 20%, due to the high level of anonymity and encryption present in the Darknet, which makes it difficult to extract features for classification. The authors analysed several quantitative values, including accuracy rates ranging from 60% to 97%, simplicity of execution ranging from 1 to 9 steps, real-time implementation ranging from less than 1 second to over 60 seconds, unknown traffic identification ranging from 30% to 95%, encrypted traffic classification ranging from 30% to 95%, and time and space complexity ranging from O(1) to O(2n). The study examined various approaches used to classify traffic in the Darknet, including machine learning, deep learning, and hybrid methods. The authors found that deep learning algorithms were effective in accurately classifying traffic on the Darknet, but the lack of labelled data and the dynamic nature of the Darknet limited their use. Despite these challenges, the study concluded that proper traffic classification is crucial for identifying malicious activity and improving the security of the Darknet. Overall, the study suggests that, although significant challenges remain, there is potential for further development and improvement of network traffic classification in the Darknet.
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