Visible to the public Detecting Cyber Threats in Non-English Hacker Forums: An Adversarial Cross-Lingual Knowledge Transfer Approach

TitleDetecting Cyber Threats in Non-English Hacker Forums: An Adversarial Cross-Lingual Knowledge Transfer Approach
Publication TypeConference Paper
Year of Publication2020
AuthorsEbrahimi, M., Samtani, S., Chai, Y., Chen, H.
Conference Name2020 IEEE Security and Privacy Workshops (SPW)
Date PublishedMay 2020
PublisherIEEE
ISBN Number978-1-7281-9346-5
Keywordsadversarial learning, Computer hacking, cross-lingual knowledge transfer, dark web, generative adversarial networks, hacker forums, Human Behavior, human factors, Knowledge engineering, knowledge transfer, Long short-term memory, machine learning algorithms, privacy, pubcrawl, Semantics
Abstract

The regularity of devastating cyber-attacks has made cybersecurity a grand societal challenge. Many cybersecurity professionals are closely examining the international Dark Web to proactively pinpoint potential cyber threats. Despite its potential, the Dark Web contains hundreds of thousands of non-English posts. While machine translation is the prevailing approach to process non-English text, applying MT on hacker forum text results in mistranslations. In this study, we draw upon Long-Short Term Memory (LSTM), Cross-Lingual Knowledge Transfer (CLKT), and Generative Adversarial Networks (GANs) principles to design a novel Adversarial CLKT (A-CLKT) approach. A-CLKT operates on untranslated text to retain the original semantics of the language and leverages the collective knowledge about cyber threats across languages to create a language invariant representation without any manual feature engineering or external resources. Three experiments demonstrate how A-CLKT outperforms state-of-the-art machine learning, deep learning, and CLKT algorithms in identifying cyber-threats in French and Russian forums.

URLhttps://ieeexplore.ieee.org/document/9283883
DOI10.1109/SPW50608.2020.00021
Citation Keyebrahimi_detecting_2020