Detecting cyber threats in non-english dark net markets: A cross-lingual transfer learning approach

Mohammadreza Ebrahimi, Mihai Surdeanu, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Recent advances in proactive cyber threat intelligence rely on early detection of cyber threats in hacker communities. Dark Net Markets (DNMs) are growing platforms in hacker community that provide hackers with highly- specialized tools and products which may not be found in other platforms. While text classification techniques have been used for cyber threat detection in English DNMs, the task is hindered in non-English platforms due to the language barrier and lack of ground-truth data. Current approaches use monolingual models on machine translated data to overcome these challenges. However, the translation errors can deteriorate the classification results. The abundance of data in English DNMs can be leveraged in learning non-English threats without using machine translation. In this study, we show that a deep cross-lingual model that can jointly learn the common language representation from two languages, significantly outperforms a monolingual model learned on machine translated data for identifying cyber threats in non-English DNMs. Unlike most studies, our approach does not require any external data source such as bilingual word embeddings or bilingual lexicons. Our experiments on Russian DNMs show that this approach can achieve better performance than state-of-the-art methods for non-English cyber threat detection in malicious hacker community.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018
EditorsDongwon Lee, Ghita Mezzour, Ponnurangam Kumaraguru, Nitesh Saxena
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages85-90
Number of pages6
ISBN (Electronic)9781538678480
DOIs
StatePublished - Dec 24 2018
Event16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018 - Miami, United States
Duration: Nov 9 2018Nov 11 2018

Publication series

Name2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018

Other

Other16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018
CountryUnited States
CityMiami
Period11/9/1811/11/18

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Keywords

  • Cross-lingual transfer learning
  • Cyber threat
  • Dark Net Markets
  • Deep learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Communication

Cite this

Ebrahimi, M., Surdeanu, M., & Chen, H. (2018). Detecting cyber threats in non-english dark net markets: A cross-lingual transfer learning approach. In D. Lee, G. Mezzour, P. Kumaraguru, & N. Saxena (Eds.), 2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018 (pp. 85-90). [8587404] (2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISI.2018.8587404