Dark web data classification using deep neural network Online publication date: Fri, 01-Mar-2024
by P.J. Sathish Kumar; J. Jency Rubia; R. Anitha; Sheshang Degadwala
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 16, No. 2, 2024
Abstract: The dark web is an overlay network comprised of the darknet, which can only be accessed via specialised software and a predetermined permission scheme. This article investigates the development of dark web intelligence as a means of enhancing cybercrime prevention tactics in several countries. On the basis of machine learning, we develop, analyse, and assess the effectiveness of darknet traffic detection systems (DTDS) in IoT networks. We focused at the safety features that are available to users, as well as their motivations and the ability to revoke their anonymity. In addition, we perform a depth analysis by automating the process of detecting hostile intent from the darknet. Finally, we compared our proposed system to various already existing DTDS models and showed that our best results are an improvement of between 1.9% and 27% over the models that were previously considered to be state-of-the-art.
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