Pith Number

pith:N44HF7KP

pith:2026:N44HF7KPJEIM2MPTVCYWM3HSJE

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Federated Stream-Processing and Latency-Gated Response for Cross-Sector Threat Detection and Collaborative Containment

Namit Mohale

A federated stream-processing framework detects coordinated cross-sector threats and achieves containment in 12-20 seconds despite network partitions.

arxiv:2605.17325 v1 · 2026-05-17 · cs.CR

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

By utilizing a stateless Pre-Filtering Dispatcher Subsystem (PFDS), in-memory lock-sharded state workers, and a 95% statistical watermark heuristic, our system maintains detection momentum during network partitions to evacuate speculative alerts and achieves total end-to-end operational convergence within a realistic 12-20 seconds window.

C2weakest assumption

The 500,000 events per second synthetic workload and prototype implementation in Go accurately represent the challenges, data patterns, and operational conditions of real-world multi-sector threat detection and collaborative containment.

C3one line summary

A federated stream-processing system with PFDS, in-memory sharded workers, and statistical watermarking achieves end-to-end cross-sector threat detection and containment in 12-20 seconds on a 500k events/sec prototype workload.

References

12 extracted · 12 resolved · 0 Pith anchors

[1] T. Akidau, R. Bradshaw, C. Chambers, S. Chernyak, R.J. Fernández- Moctezuma, R. Lax et al. ‘‘The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbo 2015

[2] O. Babayomi and D.-S. Kim. ‘‘Federated Anomaly Detection and Mit- igation for EV Charging Forecasting Under Cyberattacks’’, 2025. /em- phInternational Conference on Information and Communication Tech- 2025 · doi:10.1109/ictc66702.2025.11388140

[3] Enhancing Digital Image Forgery Detection Us- ing Transfer Learning 2024 · doi:10.1109/ac-

[4] K. Thirasak, T. Chuaphanngam, D. Chainarong and S. Fugkeaw, ‘‘TF2ML: Threat Filtering With Two-Stage Machine Learning for Effi- cient Provenance-Aware Threat Detection and Response’’,IEEE Open Journal 2025 · doi:10.1109/ojcs.2025.3618157

[5] M. Barni and F. Bartolini,Watermarking Systems Engineering: Enabling Digital Assets Security and Other Applications, CRC Press, 2024 2024

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:52.221401Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6f3872fd4f4910cd31f3a8b1666cf2490ed3b33c30ca30b60223f98f173bdc52

Aliases

arxiv: 2605.17325 · arxiv_version: 2605.17325v1 · doi: 10.48550/arxiv.2605.17325 · pith_short_12: N44HF7KPJEIM · pith_short_16: N44HF7KPJEIM2MPT · pith_short_8: N44HF7KP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/N44HF7KPJEIM2MPTVCYWM3HSJE \
  | 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: 6f3872fd4f4910cd31f3a8b1666cf2490ed3b33c30ca30b60223f98f173bdc52

Canonical record JSON

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    "abstract_canon_sha256": "098f3b078010a1bd442ac3bf75bccd6ce4193e5c715366589372b935f8e1b894",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-05-17T08:32:08Z",
    "title_canon_sha256": "4e76fa505b63076cf7b429a771ff5a7dec93c907c42a680771919304de084bfc"
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    "kind": "arxiv",
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