INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 6/8/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
FDM: A Framework for Decision-making to build ML-based Malware detection systems arXiv:2606.06894v1 Announce Type: new Abstract: Selecting appropriate machine learning (ML) configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering, and update mechanisms must jointly satisfy operational constraints that vary across deployment contexts. This paper proposes the Framework for Decision-making (FDM) to build ML-based malware detec…
https://arxiv.org/abs/2606.06894
sha256:635bd54da5d117ab640fb3e909d635c05532aedea94fab69f7e32ff008b277c2
What we pulled out
Deterministic extractor (IOC + allowlisted tokens + ATT&CK IDs present in DB).
Indicators
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No indicators linked for this report.
Malware families
Allowlist token matches only.
Threat actors mentioned
Allowlist mentions — not a formal attribution verdict.
ATT&CK techniques
MITRE IDs referenced in text and present in local technique table.
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