Paper Citation Record · LEDGER

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2606.31594.

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

pith.paper-citation-record.v1
2606.31594 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-01T05:23:43.258718Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

33 of 33 outbound references displayed

verified exact1
verified fuzzy28
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 177d80fe-416f-4821-a945-f05af435125d · outbound

This paper cites Internet-of-things security and vulnerabilities: Case study.Journal of Applied Security Research, 18(3):559–575, 2023.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Internet-of-things security and vulnerabilities: Case study.Journal of Applied Security Research, 18(3):559–575, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.063452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e77cda6f-360a-4b81-b231-df53a03edd06 · outbound

This paper cites Internet of things: Applications and challenges in technol- ogy and standardization.Wireless Personal Communications, 58(1):49–69, 2011.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Internet of things: Applications and challenges in technol- ogy and standardization.Wireless Personal Communications, 58(1):49–69, 2011

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.061402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 81f13520-9c39-4bc9-a3a7-ac7048312101 · outbound

This paper cites Next-generation internet of things (iot): Opportunities, challenges, and solutions.Sensors, 21(4), 2021.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Next-generation internet of things (iot): Opportunities, challenges, and solutions.Sensors, 21(4), 2021

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.100695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 487bf37a-7dbf-437c-bc3f-23ca89e13d97 · outbound

This paper cites Hardware, software platforms, operating systems and routing protocols for internet of things applications.Wirel.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Hardware, software platforms, operating systems and routing protocols for internet of things applications.Wirel

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.072703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation deaae15c-bb16-4419-9cd5-1619d5c1dabf · outbound

This paper cites Internet of things: A survey on enabling technologies, protocols, and applications.IEEE Communi- cations Surveys & Tutorials, 17(4):2347–2376, 2015.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Internet of things: A survey on enabling technologies, protocols, and applications.IEEE Communi- cations Surveys & Tutorials, 17(4):2347–2376, 2015

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.080835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 04c69443-e4c2-4d4a-af1a-61661a230579 · outbound

This paper cites an unresolved cited work.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-07-06T22:03:00.094466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4b01e75d-731c-4c85-bd8f-c4fe913c7f42 · outbound

This paper cites Machine learning in iot security: Current solutions and future challenges.IEEE Communications Surveys & Tutorials, 22(3):1686–1721, 2020.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Machine learning in iot security: Current solutions and future challenges.IEEE Communications Surveys & Tutorials, 22(3):1686–1721, 2020

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.057292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bebed913-1036-4ef2-abbb-2d62e73756f1 · outbound

This paper cites Scanning the cycle: Timing-based authentication on plcs.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Scanning the cycle: Timing-based authentication on plcs

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.120133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1341d48c-0cb6-49a2-babb-af74bbe14b0e · outbound

This paper cites Gotham dataset 2025: A reproducible large-scale iot network dataset for intrusion detection and security research, 2025.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Gotham dataset 2025: A reproducible large-scale iot network dataset for intrusion detection and security research, 2025

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.118198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cda10eb7-0fe4-41be-a025-630464a0323c · outbound

This paper cites Gotham testbed: A reproducible iot testbed for security experiments and dataset generation.IEEE Transactions on Dependable and Secure Computing, 21(1):186–203, 2024.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Gotham testbed: A reproducible iot testbed for security experiments and dataset generation.IEEE Transactions on Dependable and Secure Computing, 21(1):186–203, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.113757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03d6fda4-b0dd-4a37-89ac-17b47a68c043 · outbound

This paper cites Internet of things and ransomware: Evolution, mitigation and prevention.Egyptian Informatics Journal, 22(1):105–117, 2021.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Internet of things and ransomware: Evolution, mitigation and prevention.Egyptian Informatics Journal, 22(1):105–117, 2021

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.111684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 692643d8-9c5c-4d8a-a6ab-f0f86e5e69ce · outbound

This paper cites Alex Halderman, Luca Invernizzi, Michalis Kallitsis, Deepak Kumar, Chaz Lever, Zane Ma, Joshua Mason, Damian Menscher, Chad Seaman, Nick Sullivan, Kurt Thomas, and Yi Zhou.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Alex Halderman, Luca Invernizzi, Michalis Kallitsis, Deepak Kumar, Chaz Lever, Zane Ma, Joshua Mason, Damian Menscher, Chad Seaman, Nick Sullivan, Kurt Thomas, and Yi Zhou

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.070717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a3da2858-3e3c-4053-b391-2cd7daa8cded · outbound

This paper cites Limitations of state estimation based cyber attack detection schemes in industrial control systems.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Limitations of state estimation based cyber attack detection schemes in industrial control systems

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.074782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ba6c0fdc-77ec-4a7f-b581-3135d2b5c916 · outbound

This paper cites Ghorbani.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Ghorbani

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.115377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2f2750ab-fadb-4469-8199-4e28a5ac81ac · outbound

This paper cites Frank Chen.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Frank Chen

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.109463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d64775db-e0f2-4678-8469-5da2a5f6f244 · outbound

This paper cites an unresolved cited work.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-07-06T22:03:00.065055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b81bd43a-fb6d-487e-9e5c-36daa1d5c01d · outbound

This paper cites A review on evolving domains of internet of things: Architecture, applications, and technical challenges.International Journal of Communication Systems, 36(18):e5613, 2023.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks A review on evolving domains of internet of things: Architecture, applications, and technical challenges.International Journal of Communication Systems, 36(18):e5613, 2023

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.088841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 034eca47-32b7-431e-9177-93f5e28bb5ee · outbound

This paper cites Machine learning for intru- sion detection in industrial control systems: challenges and lessons from experimental evaluation.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Machine learning for intru- sion detection in industrial control systems: challenges and lessons from experimental evaluation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.082897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dab5cd31-b1ec-40e7-b6e5-05581159aa04 · outbound

This paper cites Gutierrez.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Gutierrez

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.102787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43bd3f1d-6659-4862-8b9b-df0610372cc5 · outbound

This paper cites an unresolved cited work.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-07-06T22:03:00.076668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cd2400fa-fd91-4e36-862e-7f1b6515e327 · outbound

This paper cites Ghorbani.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Ghorbani

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.078603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2533a227-137c-4ebd-b495-e4f086465a25 · outbound

This paper cites Attack rules: an adversarial approach to generate attacks for industrial control systems using machine learning.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Attack rules: an adversarial approach to generate attacks for industrial control systems using machine learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.085003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c785ebb2-d8a4-4d52-85db-ed184ba5229b · outbound

This paper cites A survey on intrusion detection system in iot networks.Cyber Security and Applications, 3:100082, 2025.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks A survey on intrusion detection system in iot networks.Cyber Security and Applications, 3:100082, 2025

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.098143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc687d07-1043-465a-b836-86df900e7176 · outbound

This paper cites MQTT Version 5.0.https://docs.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks MQTT Version 5.0.https://docs

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.068889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cc9e77d7-6d5b-43b0-b9b2-7291498422d5 · outbound

This paper cites The Constrained Application Protocol (CoAP).

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks The Constrained Application Protocol (CoAP)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.092600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84393266-c9dc-4810-b503-a333ad287a1c · outbound

This paper cites Real Time Streaming Protocol (RTSP).

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Real Time Streaming Protocol (RTSP)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.067069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84c0671b-92be-4108-83c1-c83e0b8bf4dd · outbound

This paper cites O’Reilly Media, Inc., 2nd edition, 2019.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks O’Reilly Media, Inc., 2nd edition, 2019

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.104895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 71385665-8508-4e68-8e30-f35b37853d71 · outbound

This paper cites Mitchell.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Mitchell

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.059142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7a655a76-24b8-4949-afbf-d8f5b9acff31 · outbound

This paper cites Scikit- learn: Machine learning in Python.Journal of Machine Learning Research, 12:2825–2830, 2011.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Scikit- learn: Machine learning in Python.Journal of Machine Learning Research, 12:2825–2830, 2011

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.106970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 06302c17-81ba-4fb2-8105-f48717cb6d5b · outbound

This paper cites Transforming large-size to lightweight deep neural networks for iot applications.ACM Comput.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Transforming large-size to lightweight deep neural networks for iot applications.ACM Comput

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.086942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 94b4f9c6-26e5-42d8-8ee3-97508debc9ad · outbound

This paper cites Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-01T10:35:41.856174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 880cf283-31c4-476f-b431-1324f9cec0e7 · outbound

This paper cites an unresolved cited work.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-07-06T22:03:00.090684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d7d1c836-2763-406a-9e88-ae7c3a114dda · outbound

This paper cites Federated machine learning to enable intrusion detection systems in iot networks.Electronics, 14(6), 2025.

Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks Federated machine learning to enable intrusion detection systems in iot networks.Electronics, 14(6), 2025

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T22:03:00.096199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.