Threat Miner - A Text Analysis Engine for Threat Identification Using Dark Web Data

Deguara, Nathan and Paracha, Anum and Arshad, Junaid and Azad, Muhammad Ajmal (2023) Threat Miner - A Text Analysis Engine for Threat Identification Using Dark Web Data. In: 2022 IEEE International Conference on Big Data (Big Data), 17th - 20th December 2022, Osaka, Japan.

Threat_Miner_CR.pdf - Accepted Version

Download (528kB)


Cyber threats continue to grow with novel methods to attack computing systems, highlighting the need for sophisticated mechanisms and techniques to protect against such dynamic threats. Contemporary cyber defence mechanisms utilise a range of methods which rely on monitoring network or system-level events. However, with the growing use of the dark web by mal-actors to share exploits, breaches, and data leaks, the use of such information to strengthen defence mechanisms becomes an intriguing prospect. In this paper, we present our efforts to develop a text mining engine (Threat Miner) which analyses data from dark web forums and transforms it into actionable intelligence. Leveraging cutting-edge machine learning techniques and utilising a bespoke threat dictionary, Threat Miner extracts useful information from dark web forums into STIX form, enabling it to be used with threat intelligence platforms. We also present the results of a thorough evaluation
of our scheme which was conducted with the CrimeBB dataset to understand the feasibility of the approach as well as its effectiveness in strengthening defence capability against cyber threats.

Item Type: Conference or Workshop Item (Paper)
Identification Number:
31 October 2022Accepted
26 February 2023Published Online
Uncontrolled Keywords: Text mining, Dark Web, Text analysis, Dictionaries, Transforms, Machine learning, Big Data
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Junaid Arshad
Date Deposited: 06 Dec 2022 16:07
Last Modified: 22 Mar 2023 12:00

Actions (login required)

View Item View Item


In this section...