Deep Learning Algorithm for Threat Detection in Hackers Forum (Deep Web)

• URL: http://arxiv.org/abs/2202.01448v1
• Date: Thu, 3 Feb 2022 07:49:44 GMT
• Title: Deep Learning Algorithm for Threat Detection in Hackers Forum (Deep Web)
• Authors: Victor Adewopo, Bilal Gonen, Nelly Elsayed, Murat Ozer, Zaghloul Saad Elsayed
• Abstract summary: We propose a novel approach for detecting cyberthreats using a deep learning algorithm Long Short-Term Memory (LSTM) Our model can be easily deployed by organizations in securing digital communications and detection of vulnerability exposure before cyberattack.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In our current society, the inter-connectivity of devices provides easy access for netizens to utilize cyberspace technology for illegal activities. The deep web platform is a consummative ecosystem shielded by boundaries of trust, information sharing, trade-off, and review systems. Domain knowledge is shared among experts in hacker's forums which contain indicators of compromise that can be explored for cyberthreat intelligence. Developing tools that can be deployed for threat detection is integral in securing digital communication in cyberspace. In this paper, we addressed the use of TOR relay nodes for anonymizing communications in deep web forums. We propose a novel approach for detecting cyberthreats using a deep learning algorithm Long Short-Term Memory (LSTM). The developed model outperformed the experimental results of other researchers in this problem domain with an accuracy of 94\% and precision of 90\%. Our model can be easily deployed by organizations in securing digital communications and detection of vulnerability exposure before cyberattack.

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