Paper Citation Record · LEDGER

An AutoML-based approach for Network Intrusion Detection

As of 13 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2411.15920.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.15920 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:49:57.022753Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy25
unresolved1
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8e5879f6-7f39-4690-adc0-1b1a6ea76789 · outbound

This paper cites Network intrusion detection for iot security based on learning tech- niques,.

An AutoML-based approach for Network Intrusion Detection Network intrusion detection for iot security based on learning tech- niques,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.274616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3de50336-427c-4793-85bd-f49874a13205 · outbound

This paper cites Cybercrime To Cost The World $10.5 Trillion Annually By 2025 — cybersecurityventures.com,.

An AutoML-based approach for Network Intrusion Detection Cybercrime To Cost The World $10.5 Trillion Annually By 2025 — cybersecurityventures.com,

Reference 2

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T13:49:57.266942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 33cd9f68-be8c-4b50-a731-0c53aa6da19d · outbound

This paper cites Cost benefits of using machine learning features in nids for cyber security in uk small medium enterprises (sme),.

An AutoML-based approach for Network Intrusion Detection Cost benefits of using machine learning features in nids for cyber security in uk small medium enterprises (sme),

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.258918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fee92c26-baec-4fdc-956c-f1ed826dabbe · outbound

This paper cites Cost of a data breach 2024 | IBM — ibm.com,.

An AutoML-based approach for Network Intrusion Detection Cost of a data breach 2024 | IBM — ibm.com,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.250558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1c99a030-3dec-46e0-8151-457544a02eed · outbound

This paper cites Com- parative research on network intrusion detection methods based on machine learning,.

An AutoML-based approach for Network Intrusion Detection Com- parative research on network intrusion detection methods based on machine learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.242563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 756bc589-2530-47fc-bb4d-f4af5c698911 · outbound

This paper cites A signature-based intrusion detection system for the internet of things,.

An AutoML-based approach for Network Intrusion Detection A signature-based intrusion detection system for the internet of things,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.234257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9e45c0c7-13cc-46f4-ab12-0b481d9eea8e · outbound

This paper cites Network intrusion detection system: A machine learning approach,.

An AutoML-based approach for Network Intrusion Detection Network intrusion detection system: A machine learning approach,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.226018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 043ccd53-b314-4ef4-b995-cede3fb591e0 · outbound

This paper cites A new intrusion detection sys- tem based on knn classification algorithm in wireless sensor network,.

An AutoML-based approach for Network Intrusion Detection A new intrusion detection sys- tem based on knn classification algorithm in wireless sensor network,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.217727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 820749b8-fd14-4035-a362-8cf28874a1af · outbound

This paper cites Network intrusion detection system: a survey on artificial intelligence-based techniques,.

An AutoML-based approach for Network Intrusion Detection Network intrusion detection system: a survey on artificial intelligence-based techniques,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.209795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 345dc414-2649-4603-af12-cc30c27e0678 · outbound

This paper cites The Untold Story of NotPetya, the Most Devastating Cyberattack in History — wired.com,.

An AutoML-based approach for Network Intrusion Detection The Untold Story of NotPetya, the Most Devastating Cyberattack in History — wired.com,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.201808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f39b3168-7367-4029-9c1e-b5e9d1e7dd13 · outbound

This paper cites Ad- vancing cybersecurity: a comprehensive review of ai-driven detection techniques,.

An AutoML-based approach for Network Intrusion Detection Ad- vancing cybersecurity: a comprehensive review of ai-driven detection techniques,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.193741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 24032c0f-d768-4c14-a1fb-67b4a936e414 · outbound

This paper cites Survey on intrusion detection system using machine learning techniques,.

An AutoML-based approach for Network Intrusion Detection Survey on intrusion detection system using machine learning techniques,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.185692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7dfc719c-7e3c-4b25-967c-845ef3a7a898 · outbound

This paper cites A novel ensembled technique for anomaly detection,.

An AutoML-based approach for Network Intrusion Detection A novel ensembled technique for anomaly detection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.177067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d1a43d26-24da-4f90-aaf2-b87d0a606c81 · outbound

This paper cites Automl accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke,.

An AutoML-based approach for Network Intrusion Detection Automl accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.168916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e0825309-7fba-4c71-b7f9-563d99136249 · outbound

This paper cites Automl: A systematic review on automated machine learning with neural architecture search,.

An AutoML-based approach for Network Intrusion Detection Automl: A systematic review on automated machine learning with neural architecture search,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.160783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bf072466-730e-4836-b1bd-be6f6929dfd1 · outbound

This paper cites Hutter, L.

An AutoML-based approach for Network Intrusion Detection Hutter, L

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.152431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d9f5be81-3395-4efe-87df-3025388d4c37 · outbound

This paper cites Mljar: State-of-the-art automated machine learning framework for tabular data. version 0.10.3,.

An AutoML-based approach for Network Intrusion Detection Mljar: State-of-the-art automated machine learning framework for tabular data. version 0.10.3,

Reference 17

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T13:49:57.143857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c1f573c5-0d83-493b-92e4-be7832e471ae · outbound

This paper cites A machine learning-based intrusion de- tection system for securing remote desktop connections to electronic flight bag servers,.

An AutoML-based approach for Network Intrusion Detection A machine learning-based intrusion de- tection system for securing remote desktop connections to electronic flight bag servers,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.126925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9e69576c-c3f0-475b-806b-247a8eb173ee · outbound

This paper cites Network anomaly detection using exponential random graph models and autoregressive moving average,.

An AutoML-based approach for Network Intrusion Detection Network anomaly detection using exponential random graph models and autoregressive moving average,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.117330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b92605c1-6002-4a5f-bcf9-77e171a5d874 · outbound

This paper cites A detection method for anomaly flow in software defined network,.

An AutoML-based approach for Network Intrusion Detection A detection method for anomaly flow in software defined network,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.107545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 32b24736-9ef7-4591-9072-9fee704f5911 · outbound

This paper cites A novel model for anomaly detection in network traffic based on kernel support vector machine,.

An AutoML-based approach for Network Intrusion Detection A novel model for anomaly detection in network traffic based on kernel support vector machine,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.097679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f4c47f63-82c6-422f-b833-c4c2be315864 · outbound

This paper cites Group-wise principal component analysis for exploratory intrusion detection,.

An AutoML-based approach for Network Intrusion Detection Group-wise principal component analysis for exploratory intrusion detection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.088357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 84076e86-b477-4731-8d72-21cb04f64db7 · outbound

This paper cites A lightweight supervised intrusion detection mechanism for iot networks,.

An AutoML-based approach for Network Intrusion Detection A lightweight supervised intrusion detection mechanism for iot networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.079296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1d24d74b-2c09-4b5e-9703-c7198b2265d5 · outbound

This paper cites Dfaid: Density-aware and feature-deviated active intrusion detection over network traffic streams,.

An AutoML-based approach for Network Intrusion Detection Dfaid: Density-aware and feature-deviated active intrusion detection over network traffic streams,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.070125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ab1ce3ec-78a8-4623-9ea3-86aba4da10ac · outbound

This paper cites A study on nsl-kdd dataset for intrusion detection system based on classification algorithms,.

An AutoML-based approach for Network Intrusion Detection A study on nsl-kdd dataset for intrusion detection system based on classification algorithms,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T13:49:57.017390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa372177-359b-4093-b95f-fb6207357a03 · outbound

This paper cites Exploring discrepancies in findings obtained with the kdd cup’99 data set,.

An AutoML-based approach for Network Intrusion Detection Exploring discrepancies in findings obtained with the kdd cup’99 data set,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.055747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e7e8e0a9-2ff6-4b13-ae97-19140a2eb37a · outbound

This paper cites Machine learning for network intrusion detection—a comparative study,.

An AutoML-based approach for Network Intrusion Detection Machine learning for network intrusion detection—a comparative study,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.046702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9cd09362-6630-442a-8a81-4557c9b74bd6 · outbound

This paper cites Available: https://github.com/mljar/mljar-supervised.

An AutoML-based approach for Network Intrusion Detection Available: https://github.com/mljar/mljar-supervised

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:49:57.135524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.