Paper Citation Record · LEDGER

Optimized IoT Intrusion Detection using Machine Learning Technique

As of 13 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2412.02845.

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

pith.paper-citation-record.v1
2412.02845 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:05:42.367203Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:56:40.252597Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T00:16:22.474752Z

Reference resolution

14 of 14 outbound references displayed

verified exact2
verified fuzzy12
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3a6eba41-b9dd-44f8-a76b-af130d1b3931 · outbound

This paper cites Future internet: the internet of things architecture, possible applications and key challenges,.

Optimized IoT Intrusion Detection using Machine Learning Technique Future internet: the internet of things architecture, possible applications and key challenges,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.884374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4ead98ca-9890-4fed-b92b-db19842434cb · outbound

This paper cites IoT under attack: Kaspersky hon- eypot logs 1.5 billion attacks in H1 2021,.

Optimized IoT Intrusion Detection using Machine Learning Technique IoT under attack: Kaspersky hon- eypot logs 1.5 billion attacks in H1 2021,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.850076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2c18f6f1-c2a7-4132-b04f-6132b784c323 · outbound

This paper cites Deep Learning-Based Rule-Based Feature Selection for Intru- sion Detection in Industrial Internet of Things Networks,.

Optimized IoT Intrusion Detection using Machine Learning Technique Deep Learning-Based Rule-Based Feature Selection for Intru- sion Detection in Industrial Internet of Things Networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.795867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 479bcaa9-f331-4aa1-bb9d-207b469b9df0 · outbound

This paper cites An ensemble learning based intrusion detection model for industrial IoT security,.

Optimized IoT Intrusion Detection using Machine Learning Technique An ensemble learning based intrusion detection model for industrial IoT security,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.764664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 42d10275-9166-4045-9c82-6469275e45bf · outbound

This paper cites Machine learning-based adaptive synthetic sampling technique for intrusion de- tection,.

Optimized IoT Intrusion Detection using Machine Learning Technique Machine learning-based adaptive synthetic sampling technique for intrusion de- tection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.737025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 581a0ddb-5bde-41e7-bef7-e95215fa0bb6 · outbound

This paper cites Deep learning approach for SDN-enabled intrusion detection system in IoT networks,.

Optimized IoT Intrusion Detection using Machine Learning Technique Deep learning approach for SDN-enabled intrusion detection system in IoT networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.700084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2e234c8c-ed53-4b75-b5ed-83852530bf07 · outbound

This paper cites Hybrid deep learning-based intrusion detection system for RPL IoT networks,.

Optimized IoT Intrusion Detection using Machine Learning Technique Hybrid deep learning-based intrusion detection system for RPL IoT networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.635337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 942f9094-195e-4416-8dcd-5bab15b32546 · outbound

This paper cites Intrusion detection using network traffic profiling and machine learning for IoT,.

Optimized IoT Intrusion Detection using Machine Learning Technique Intrusion detection using network traffic profiling and machine learning for IoT,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.596410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 61fe715f-7ec8-4910-8eb5-2e0413a0f173 · outbound

This paper cites Intrusion detection system using feature extraction with machine learning algorithms in IoT,.

Optimized IoT Intrusion Detection using Machine Learning Technique Intrusion detection system using feature extraction with machine learning algorithms in IoT,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.570431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1fa4754b-b24d-445d-842a-69d6b9111ae3 · outbound

This paper cites A hybrid deep learning- based intrusion detection system for IoT networks,.

Optimized IoT Intrusion Detection using Machine Learning Technique A hybrid deep learning- based intrusion detection system for IoT networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.545154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c9e51821-3b41-48e8-b840-45d6af41d046 · outbound

This paper cites IoT Dataset for Intrusion Detection Systems (IDS),.

Optimized IoT Intrusion Detection using Machine Learning Technique IoT Dataset for Intrusion Detection Systems (IDS),

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.520789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4c779522-9821-41cb-8db1-8f9b369127cb · outbound

This paper cites Gradient boosting classification explained through python,.

Optimized IoT Intrusion Detection using Machine Learning Technique Gradient boosting classification explained through python,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:05:42.493909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 65be8aec-5d3c-4e90-9bb9-108b8b59e0d2 · outbound

This paper cites Deep Learning Approach for Enhancing Oral Squamous Cell Carcinoma with LIME Explainable AI Technique.

Optimized IoT Intrusion Detection using Machine Learning Technique Deep Learning Approach for Enhancing Oral Squamous Cell Carcinoma with LIME Explainable AI Technique

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:05:42.464739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aac700e5-ba9e-4328-948b-17cfb54f6e71 · outbound

This paper cites Advance Transfer Learning Approach for Identification of Multiclass Skin Disease with LIME Explainable AI Technique,.

Optimized IoT Intrusion Detection using Machine Learning Technique Advance Transfer Learning Approach for Identification of Multiclass Skin Disease with LIME Explainable AI Technique,

Reference 14

Resolution: verified exact; doi, observed 2026-08-11T23:05:42.421717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 8d1525d7-65dd-4c05-94d2-d9e1c617fb33 · inbound

Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking cites this paper.

Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking Optimized IoT Intrusion Detection using Machine Learning Technique

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-11T21:56:40.300378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.