Abstract
Current era is the age of digital technology with LTE running over it. Ranging from smaller to bigger tasks, every bit of information is being stored and channelized through Internet which becomes a huge reservoir of data. This information became a point of interest for digital users present in the backdoors of Internet, which we call as black hat hackers. These hackers have come up with their own network, where they can perform illegal activities without even being noticed in their network, usually called as darknet. Every unofficial work like drug dealings, ammunition supply, human trafficking, credit card detail sharing, etc., is done here. Traces of these activities are not present because of the inaccessible nature of darknet. The architecture of these Websites is totally different from the normal network we work on, and it is therefore not possible to access such Websites like Tor and Onion directly. Therefore, there is a prior need for the detection of traffic coming from the darknet. In this paper, different algorithms have been used which classifies the darknet traffic based on varied features. The algorithms that are being used are K-nearest neighbor (KNN), support vector machine (SVM), Naïve Bayes classifier (NB), and random forest (RF). Out of these algorithms, random forest performs the best with the accuracy of 98.7.
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Mantoo, B.A., Bansal, H. (2023). Darkness Behind the Darknet: A Comparative Analysis of Darknet Traffic and the Crimes Behind the Darknet Using Machine Learning. In: Singh, Y., Singh, P.K., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Proceedings of International Conference on Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 1001. Springer, Singapore. https://doi.org/10.1007/978-981-19-9876-8_12
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DOI: https://doi.org/10.1007/978-981-19-9876-8_12
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