pith:EB6I22WJ
Characterizing AI-Assisted Bot Traffic in Darknet Data: Implications for ICS and IIoT Security
AI-assisted bots use micro-pacing delays to evade 97.47% of standard volumetric IDS thresholds while ICS port targeting nearly doubles.
arxiv:2605.14209 v1 · 2026-05-14 · cs.CR · cs.NI
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Claims
Our simulated anomaly-based IDS demonstrates that these evasion techniques enable 97.47% of modern bot traffic to bypass standard volumetric thresholds undetected. Compensatory sensitivity tuning triggers a 68.10% false-positive rate.
The assumption that the Merit ORION darknet dataset is representative of reconnaissance traffic that would actually reach and target operational ICS/IIoT devices, and that the simulated IDS accurately models real-world deployment conditions and thresholds.
Darknet analysis shows ICS bot traffic doubling from 0.82% to 1.51% over four years, with micro-pacing enabling 97.47% evasion of standard volumetric IDS thresholds.
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Receipt and verification
| First computed | 2026-05-17T23:39:10.947736Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
207c8d6ac9982c8181ff1d280b677cda507eba214a8f02003845a49b62aef0e8
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/EB6I22WJTAWIDAP7DUUAWZ343J \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 207c8d6ac9982c8181ff1d280b677cda507eba214a8f02003845a49b62aef0e8
Canonical record JSON
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