INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 6/2/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
Improving IoT Intrusion Detection Through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data arXiv:2606.00161v1 Announce Type: new Abstract: The detection of intrusions in IoT-based networks poses challenges that cannot be overcome using traditional machine learning methods. Perhaps the biggest of them is related to the presence of a class imbalance in the side-channel dataset, where the number of samples in the normal class compared to t…
https://arxiv.org/abs/2606.00161
sha256:75a6abc93dc672fb3c4640da6f8ea14773ffbd7df1bd686dcc051e6bc9192f38
What we pulled out
Deterministic extractor (IOC + allowlisted tokens + ATT&CK IDs present in DB).
Indicators
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Malware families
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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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