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

arXiv: https://arxiv.org/abs/2202.01448 · HTML: https://mlsanity.com/paper/2202.01448/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 48 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Predicting Cyber Events by Leveraging Hacker Sentiment](https://mlsanity.com/paper/1804.05276/) (2018)
- [Plunge into the Underworld: A Survey on Emergence of Darknet](https://mlsanity.com/paper/2001.02300/) (2020)

## Built on by (most cited first, 2 of 2)

- [Review on Action Recognition for Accident Detection in Smart City Transportation Systems](https://mlsanity.com/paper/2208.09588/) (2022)
- [AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities](https://mlsanity.com/paper/2307.12128/) (2023)

## Abstract (arXiv)

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.