Pith Number

pith:P4BY4YZK

pith:2026:P4BY4YZKZJOJUIE42MGAZJLMGP

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XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles

Christos Douligeris, Iakovos-Christos Zarkadis

XGBoost with SHAP and statistical tests shows density support intersections cause misclassifications in Wormhole and Blackhole attacks on UAVIDS-2025

arxiv:2605.13922 v1 · 2026-05-13 · cs.CR · cs.LG · stat.CO

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Claims

C1strongest claim

With our top-performing model, XGBoost, we proceed to Shapley Additive explanations (SHAP), to analyze the global and local feature importances and understand which features, each attack targets, to mimic normal traffic and where the misclassifications occur. ... we discover the true causes of false predictions, observed in Wormhole and Blackhole attacks in UAVIDS-2025.

C2weakest assumption

The UAVIDS-2025 dataset accurately represents real-world UAV traffic distributions and that the chosen statistical tests (Westfall-Young, Jensen-Shannon on KDEs) correctly attribute misclassifications to density support intersection rather than model or preprocessing artifacts.

C3one line summary

XGBoost with SHAP and statistical distribution analysis on UAVIDS-2025 identifies density support intersection as the cause of false predictions for Wormhole and Blackhole attacks in UAV intrusion detection.

References

29 extracted · 29 resolved · 2 Pith anchors

[1] Machine Learning Based In- trusion Detection System 2019 · doi:10.1109/icoei.2019.8862784

[2] A Taxonomy of Machine-Learning-Based Intrusion Detection Systems for the Internet of Things: A Survey 2021 · doi:10.1109/jiot.2021.3126811

[3] Securing UA V Swarms with Vision Transform- ers: A Byzantine-Robust Federated Learning Framework for Cross- Modal Intrusion Detection · doi:10.3390/drones10020125

[4] Better by default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data 2024

[5] In: 2024 IEEE Inter- national Conference on Cyber Security and Resilience (CSR) 2024 · doi:10.1109/ccnc51664.2024.10454862

Receipt and verification

First computed	2026-05-17T23:39:15.561224Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7f038e632aca5c9a209cd30c0ca56c33c2540eb5e3df4ddac3959e1663fd8f04

Aliases

arxiv: 2605.13922 · arxiv_version: 2605.13922v1 · doi: 10.48550/arxiv.2605.13922 · pith_short_12: P4BY4YZKZJOJ · pith_short_16: P4BY4YZKZJOJUIE4 · pith_short_8: P4BY4YZK

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/P4BY4YZKZJOJUIE42MGAZJLMGP \
  | 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: 7f038e632aca5c9a209cd30c0ca56c33c2540eb5e3df4ddac3959e1663fd8f04

Canonical record JSON

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    "abstract_canon_sha256": "3dca79007eb424612b7ce8c65bb9c55631c1965c543027c830b8921f7c682697",
    "cross_cats_sorted": [
      "cs.LG",
      "stat.CO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-05-13T14:08:36Z",
    "title_canon_sha256": "a492f4d6d48a7f8c27a80be80853ba8d0a4793f2304ef70025e0c2069f305d5f"
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