Title: Dark web data classification using deep neural network

Authors: P.J. Sathish Kumar; J. Jency Rubia; R. Anitha; Sheshang Degadwala

Addresses: Department of Computer Science and Engineering, Panimalar Engineering College, Chennai – 600123, India ' Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering, Trichy, Tamil Nadu, India ' Department of Computer Applications, S.A. Engineering College, Chennai, Tamil Nadu, India ' Department of Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India

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.

Keywords: darknet; traffic analysis; network management; deep learning neural networks; real-time forensics; darknet traffic detection systems; DTDS.

DOI: 10.1504/IJESDF.2024.137033

International Journal of Electronic Security and Digital Forensics, 2024 Vol.16 No.2, pp.202 - 212

Received: 29 Sep 2022
Accepted: 24 Nov 2022
Published online: 01 Mar 2024 *

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