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arXiv Computation and Language By Fajar Wijitrisnanto (National Cyber and Crypto Agency, Jakarta, Indonesia), Alsharif Abuadbba (CSIRO, Sydney, Australia), Yansong Gao (CSIRO, Sydney, Australia, The University of Western Australia, Perth, Australia), Nan Wu (CSIRO, Sydney, Australia)

DisCTI: Who Needs to Know Timely? Automated Sector-Aware Cyber Threat Intelligence Dissemination

Read the original on arXiv Computation and Language →

The paper introduces DisCTI, a system that automatically maps cyber threat intelligence (CTI) events to relevant industry sectors using a multilabel classification approach. By creating a dataset of 872 sector‑labelled CTI events and applying a BERT transformer model, the authors achieve a macro‑averaged F1‑score of 0.89, correctly assigning 94.5% of sector labels. This demonstrates that embedding expert knowledge into machine learning can enable timely, sector‑aware CTI dissemination, improving defensive response.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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