Paper Citation Record · LEDGER

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures

As of 3 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2605.26166.

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

pith.paper-citation-record.v1
2605.26166 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T23:33:05.021896Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+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 exact2
verified fuzzy0
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 34c776a2-3e82-4cfd-b790-baaf4c3fbb16 · outbound

This paper cites Applications of IoT in the auto- motive industry,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Applications of IoT in the auto- motive industry,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Unavailable: canonical work link unavailable.

Observation b3a1c9c3-a9a7-428a-96db-c4452c579971 · outbound

This paper cites IoT for smart healthcare,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures IoT for smart healthcare,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation 6658b0f6-353b-4b6b-933c-cf7983c85281 · outbound

This paper cites Internet of things for smart cities,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Internet of things for smart cities,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation 48475a78-e4b7-45f9-9476-d42b40bf4f05 · outbound

This paper cites Worldwide IoT malware attack statistics,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Worldwide IoT malware attack statistics,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation db312253-7f2e-4f17-9c3a-0c80f57e356c · outbound

This paper cites Available:https://www.statista.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Available:https://www.statista

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:34:04.533049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 53db74ea-f9f4-4cbd-a052-dba47a4c3038 · outbound

This paper cites A lightweight concept drift de- tection framework,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures A lightweight concept drift de- tection framework,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

Source-reported events for the cited work

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Observation 1baf4f67-7c09-40c5-ad80-19d0ee05f86c · outbound

This paper cites Anomaly-based network intru- sion detection,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Anomaly-based network intru- sion detection,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

Source-reported events for the cited work

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Observation 1971e7f7-1db3-46d1-a859-33dcd29c566e · outbound

This paper cites Deep learning for cyber security in- trusion detection,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Deep learning for cyber security in- trusion detection,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

Source-reported events for the cited work

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Observation 1eb65f41-759c-4fb3-aa59-6b2e77c5f67b · outbound

This paper cites AOC-IDS: Autonomous online frame- work with contrastive learning for intrusion detection,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures AOC-IDS: Autonomous online frame- work with contrastive learning for intrusion detection,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51d90568-8253-4804-a194-57de83efeb2d · outbound

This paper cites Network IDS: A survey on AI-based techniques,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Network IDS: A survey on AI-based techniques,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

Source-reported events for the cited work

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Observation a3079c2e-843b-4441-8acd-d919d7290b23 · outbound

This paper cites PCA and SVM based IDS,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures PCA and SVM based IDS,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation 70d2761b-f0ad-4dcb-bc20-29e676661b55 · outbound

This paper cites Performance comparison of SVM, RF, and ELM for IDS,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Performance comparison of SVM, RF, and ELM for IDS,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

Source-reported events for the cited work

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Observation 4b60070c-d2c3-475e-b3f9-2bf0eedc5c17 · outbound

This paper cites Applying CNN for network intru- sion detection,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Applying CNN for network intru- sion detection,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

Source-reported events for the cited work

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Observation 39a25077-1858-4a0f-ba64-f3bf9680ec23 · outbound

This paper cites Deep learning for IDS using RNNs,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Deep learning for IDS using RNNs,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation d4a05966-9d06-49e5-832f-b8300a1dc89d · outbound

This paper cites FeCo: Boosting IDS in IoT via contrastive learning,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures FeCo: Boosting IDS in IoT via contrastive learning,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation 5443a0e2-4c33-4e1f-8d09-1e1f5852bd21 · outbound

This paper cites Contrastive learning enhanced intrusion de- tection,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Contrastive learning enhanced intrusion de- tection,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation 25c1e4f7-96bf-4010-967f-32d0f27ab83d · outbound

This paper cites Contrastive learning over ran- dom Fourier features for IoT IDS,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Contrastive learning over ran- dom Fourier features for IoT IDS,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

Source-reported events for the cited work

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Observation dc1790c7-3da3-4dc0-b7e3-93f31e9622bb · outbound

This paper cites Intrusion detection in IoT under data and concept drifts,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Intrusion detection in IoT under data and concept drifts,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

Source-reported events for the cited work

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Observation 943e1060-b881-4b3f-9430-0e6e56315ee8 · outbound

This paper cites Anomaly detection in the open world,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Anomaly detection in the open world,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation ee1ec78d-1313-4eb1-959b-a8ca642827fe · outbound

This paper cites Novel online IDS for indus- trial IoT based on OI-SVDD,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Novel online IDS for indus- trial IoT based on OI-SVDD,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Unavailable: canonical work link unavailable.

Observation 8cd50e13-3035-4307-a812-ae0a2c956cd6 · outbound

This paper cites SMOTE: Synthetic minority over- sampling technique,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures SMOTE: Synthetic minority over- sampling technique,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation 73cbc25f-1c73-40a3-9e65-dabcd2c4d7c3 · outbound

This paper cites XGBoost: A scalable tree boosting system,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures XGBoost: A scalable tree boosting system,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation c36f5005-37eb-4c28-b0c2-da83d27fe216 · outbound

This paper cites Mixup: Beyond empirical risk minimiza- tion,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Mixup: Beyond empirical risk minimiza- tion,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation ca68eaf2-de8b-45b7-8e82-ddff675b4cad · outbound

This paper cites UNSW-NB15: A comprehen- sive dataset for network IDS,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures UNSW-NB15: A comprehen- sive dataset for network IDS,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation 81bc93fc-45f9-4ccc-8bc8-5f0a07825f00 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Representation Learning with Contrastive Predictive Coding

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-06-29T23:34:04.535410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a59040cb-fffa-4a3b-ab48-9b48647a55da · outbound

This paper cites EPFG: Electricity price forecasting with enhanced GANs neural network,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures EPFG: Electricity price forecasting with enhanced GANs neural network,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Observation 264acbff-f4cf-4568-af14-6751ede3bf33 · outbound

This paper cites LNDIR: A lightweight non-increasing delivery-latency interval-based routing for duty-cycled sensor networks,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures LNDIR: A lightweight non-increasing delivery-latency interval-based routing for duty-cycled sensor networks,

Reference 27

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Observation 3163520f-90c2-43a1-805b-27a1d32ebdd4 · outbound

This paper cites Steel defect classifi- cation using machine learning,.

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures Steel defect classifi- cation using machine learning,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-29T23:33:05.021896Z

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Pith citing papers

No inbound Pith citation observations are available.