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Towards Efficient Threat Intelligence Instruction Tuning via Quality-Aware and Optimal Transport-Guided Data Selection — arXiv:2610.20284

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.20284
License (as archived): CC0-1.0
Archived: 2026-10-15T21:46:42.030Z
SHA-256 of the archived text: cba62ab5406a8aa67d11b23178161494db10bbc8b012a979bd9a6185997038e6
Stable URL of this page: https://metraipolis.com/documento/dc_df45816fdb4e

How to cite

Towards Efficient Threat Intelligence Instruction Tuning via Quality-Aware and Optimal Transport-Guided Data Selection — arXiv:2610.20284 — arXiv (en), https://arxiv.org/abs/2610.20284 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_df45816fdb4e (Metraipolis archive dc_df45816fdb4e, sha256 cba62ab5406a8aa67d11b23178161494db10bbc8b012a979bd9a6185997038e6)

BibTeX

@misc{dcdf45816fdb4e,
  title = {Towards Efficient Threat Intelligence Instruction Tuning via Quality-Aware and Optimal Transport-Guided Data Selection — arXiv:2610.20284},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.20284},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_df45816fdb4e; sha256 cba62ab5406a8aa67d11b23178161494db10bbc8b012a979bd9a6185997038e6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.20284. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

High-quality data is essential for improving instruction tuning in threat intelligence analysis. However, existing methods for generating cybersecurity instruction data lack reliable quality assessment mechanisms, and gradient influence-based data selection methods primarily focus on single-sample matching, failing to cover diverse and complex security capabilities. To address these issues, we propose CTI-TDS, a Targeted Data Selection method for instruction tuning in Cyber Threat Intelligence analysis. Specifically, we first filter high-quality CTI instructions by integrating instruction style consistency and security knowledge verification. Furthermore, we formulate gradient influence as a distribution alignment problem, learning a gradient subspace of target tasks and applying entropic regularized optimal transport to optimize distribution matching between training samples and target tasks, enabling diversity-aware data selection. We conduct extensive experiments on CTIBench across multiple large language models. Experimental results demonstrate that when fine-tuning with only a small set of high-value instruction data, CTI-TDS outperforms existing data selection methods and achieves competitive performance on threat intelligence analysis tasks.

Tema degli esploratori: fiducia digitale e crittografia (elenco arXiv cs.CR, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR).
Pagina del lavoro: https://arxiv.org/abs/2610.20284 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T21:33:44.519Z dalla pagina https://arxiv.org/list/cs.CR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).