Pith Number

pith:66GCTIJ3

pith:2026:66GCTIJ3DYSCTTN4IQDH2HWFSD

not attested not anchored not stored refs resolved

An Efficient Machine Learning-based Framework for Detection and Prevention of Frauds in Telecom Networks

Mishal Shah, Praveen Hegde

Random Forest detects telecom fraud at 99.9% accuracy after data balancing.

arxiv:2605.17245 v1 · 2026-05-17 · cs.NI · cs.LG

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\usepackage{pith}
\pithnumber{66GCTIJ3DYSCTTN4IQDH2HWFSD}

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1 Bitcoin timestamp

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

RF recorded a high level of accuracy at 99.9% while XGBoost at 99.7%. RF was seen to give the highest performance with an accuracy of 99.9% and precision of 99.9%, recall of 99.9% and F1-score of 99.9%.

C2weakest assumption

The assumption that performance metrics measured after SMOTE balancing and on the same dataset used for training reflect genuine generalization to unseen fraud patterns in live telecom networks.

C3one line summary

Random Forest achieves 99.9% accuracy, precision, recall and F1-score for fraud detection on a 101k-record telecom CDR dataset after Min-Max scaling and SMOTE.

References

36 extracted · 36 resolved · 0 Pith anchors

[1] An examination of machine learning -based credit card fraud detection systems, 2024 · doi:10.30574/ijsra.2024.12.2.1456

[2] A Machine and Deep Learning Framework for Robust Health Insurance Fraud Detection and Prevention, 2023 · doi:10.48175/ijarsct-14000u

[3] ConvNets for fraud detection analysis, 2018 · doi:10.1016/j.procs.2018.01.107

[4] The Assessments Of Financial Risk Based On Renewable Energy Industry, 2024

[5] The Machine Learning Based Regression Models Analysis For House Price Prediction, 2024

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-20T00:03:47.308548Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f78c29a13b1e2429cdbc44067d1ec590d4a5064dc216deef4dfded0faebb9d44

Aliases

arxiv: 2605.17245 · arxiv_version: 2605.17245v1 · doi: 10.48550/arxiv.2605.17245 · pith_short_12: 66GCTIJ3DYSC · pith_short_16: 66GCTIJ3DYSCTTN4 · pith_short_8: 66GCTIJ3

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/66GCTIJ3DYSCTTN4IQDH2HWFSD \
  | 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: f78c29a13b1e2429cdbc44067d1ec590d4a5064dc216deef4dfded0faebb9d44

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b680743cda4f7255efc6052b29734ee4981188aa53e47ecb82682fcc949e1aee",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.NI",
    "submitted_at": "2026-05-17T04:03:36Z",
    "title_canon_sha256": "8f7e89a285814f86063cbd20320484c44161bbc22772f15abf2d565e7af32a90"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17245",
    "kind": "arxiv",
    "version": 1
  }
}