arXiv:2510. 11974v2 Announce Type: replace-cross Abstract: Cyber Threat Intelligence (CTI) is foundational to modern cybersecurity, enabling organizations to proactively defend against evolving threats.
arXiv:2605.25836v2 Announce Type: replace-cross Abstract: Extracting MITRE ATT&CK techniques from cyber threat intelligence (CTI) reports is an open-set, multi-label problem requiring both high recal...
arXiv:2607. 19742v1 Announce Type: cross Abstract: Cyber Threat Intelligence (CTI) reports richly describe real-world attack processes, but their unstructured narratives cannot be directly used for automated attack-path reasoning.
arXiv:2607. 24563v1 Announce Type: new Abstract: Security Operations Centers increasingly rely on automated mapping of Cyber Threat Intelligence reports to MITRE ATT&CK, yet extractor outputs remain fallible and are often stored without the evidence, provenance, and validation history needed to decide whether an individual mapping should be trusted.
The paper introduces AHLERT, a system that automatically extracts environment-aware hunt leads from Cyber Threat Intelligence reports. It combines a hybrid retriever—dense vector search plus multi-hop knowledge‑graph traversal seeded with MITRE ATT&CK—with ontology‑grounded retrieval‑augmented generation to constrain leads to a defender’s assets. Evaluations on public CTI reports show that AHLERT doubles mean F1 scores and achieves an effectiveness score of ~86.95% compared to off‑the‑shelf LLM models.
arXiv:2606. 31557v1 Announce Type: cross Abstract: In the evolving threat landscape, adversaries exploit software vulnerabilities to launch sophisticated attacks, challenging traditional defenses.
arXiv:2606. 18166v1 Announce Type: cross Abstract: Classifying Cyber Threat Intelligence (CTI) using MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) is essential for proactive defense, but historically required extensive human effort.
arXiv:2606. 18190v1 Announce Type: cross Abstract: Multi-stage cyberattacks span system, network, and browser logs.
arXiv:2608. 13050v1 Announce Type: cross Abstract: When a security researcher publishes a report on a cyberattack, detection engineers are supposed to turn it into working detection rules.
CTIFoundry is an agent‑native corpus scaffold designed to improve cyber threat intelligence (CTI) investigations by LLM agents. It transforms traditional CTI data—such as CVE, CWE, CAPEC, and ATT&CK—into a deterministic ontology graph with typed, traversable edges, a span‑grounded report layer that resolves entity aliases and provenance, and hybrid dense‑plus‑lexical retrieval surfaces. When integrated with a standard open‑source agent harness, CTIFoundry boosts overall F1 scores by 0.19 to 0.28 on the CTIConnect benchmark, achieving higher accuracy with fewer tool calls compared to agents using flat, retrieval‑augmented corpora.
arXiv:2608. 16775v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly being deployed in cybersecurity operations to assist cybersecurity analysts with rapid decision-making against emerging threats.
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.