Abstract
In recent years, instant messaging software has become a popular platform for hackers to exchange knowledge and discuss cybersecurity issues. To trace the source of key hackers and identify potential cybersecurity threats, it is necessary to extract relational triples from hacker dialogues in chat logs. In this paper, we propose a feasible scheme for extracting cybersecurity knowledge triples from an extensive corpus of diverse chat data. We developed a heuristic algorithm based on the BERT next sentence prediction task to separate sequential and asynchronous chat logs into shorter dialogues and disentangle these threads within them, which can improve the accuracy of the subsequent relation extraction process. We also annotated a dialogue relation extraction dataset and developed a relation extraction model tailored for cybersecurity domain. Experimental results demonstrate that our average F1 scores on the thread disentanglement task and the dialogue relation extraction task are 74.9 and 88.4, respectively.
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Acknowledgements
This work was supported in part by National Key Research and Development Program of China (No.2021YFB3100500) and Sichuan Science and Technology Program (No.2023YFG0162).
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Yang, Z., Huang, C., Liu, J. (2023). Unveiling Cybersecurity Threats from Online Chat Groups: A Triple Extraction Approach. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14120. Springer, Cham. https://doi.org/10.1007/978-3-031-40292-0_15
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