Pith Number

pith:XMSP6GVR

pith:2026:XMSP6GVRJEJABJXC6RPQQVKDP3

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Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation

Christos Douligeris, Iakovos-Christos Zarkadis

A unified feature space from four reprocessed NIDS datasets supports stable machine learning classification of attacks and high-fidelity adversarial synthetic data generation.

arxiv:2603.17717 v4 · 2026-03-18 · cs.CR · cs.AI · stat.AP · stat.ML

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\pithnumber{XMSP6GVRJEJABJXC6RPQQVKDP3}

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4 Citations open

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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

The findings provide stable ML models for intrusion detection and generative models with high fidelity and utility, by combining the Synthetic Data Vault framework, the TRTS and TSTR tests, with non-parametric statistical tests and f-divergence measures.

C2weakest assumption

That reprocessing the four source datasets into a single feature space preserves all critical attack signals and does not introduce systematic biases or information loss from the original collections.

C3one line summary

A unified multi-modal NIDS dataset from CIC-IDS-2017, CIC-IoT-2023, UNSW-NB15 and CIC-DDoS-2019 is used to train stable ML attack classifiers and to generate synthetic data whose fidelity and utility are assessed via SDV, f-divergences, TRTS/TSTR tests and non-parametric statistics.

Receipt and verification

First computed	2026-07-13T01:20:07.543794Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

bb24ff1ab1491200a6e2f45f0855437eee31374742a315d0b0edb6b0eb373b9f

Aliases

arxiv: 2603.17717 · arxiv_version: 2603.17717v4 · doi: 10.48550/arxiv.2603.17717 · pith_short_12: XMSP6GVRJEJA · pith_short_16: XMSP6GVRJEJABJXC · pith_short_8: XMSP6GVR

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/XMSP6GVRJEJABJXC6RPQQVKDP3 \
  | 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: bb24ff1ab1491200a6e2f45f0855437eee31374742a315d0b0edb6b0eb373b9f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6dc2572979ca5d8b8a2d9b8018fb58f50c1c7bb69a1b49eea122a4937cb428b3",
    "cross_cats_sorted": [
      "cs.AI",
      "stat.AP",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-03-18T13:35:02Z",
    "title_canon_sha256": "7dfb6f2135ca6cef92135b7ffe388dd052656bbb5ac04e0538f754d5ed954e6b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.17717",
    "kind": "arxiv",
    "version": 4
  }
}