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Paper Citation Record · LEDGER

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection

As of 22 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2412.19830.

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

pith.paper-citation-record.v1
2412.19830 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:30:13.860530Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:50:59.882257Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T04:50:59.942260Z

Reference resolution

32 of 32 outbound references displayed

  • verified exact1
  • verified fuzzy20
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5a663f88-514e-442d-b9a9-f23640e99fc3 · outbound

This paper cites Internet of things (iot)—statistics & facts,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Internet of things (iot)—statistics & facts,

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.473615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.698471Z digest=sha256:eb9c135cd654cb9d18c5df765ecd93416f71d4c4b7b0f174899c0e6c5b7772e4

Observation 8471fc77-42a8-4c54-ba89-bc7e9b813d56 · outbound

This paper cites RAG based Question-Answering for Contextual Response Prediction System.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection RAG based Question-Answering for Contextual Response Prediction System

Reference 2

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no resolver link, observed 2026-08-11T11:30:13.704000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.704000Z digest=sha256:65586f046e5587f6afe063bea5527adaa7650c462f6520dede670b4870baa985

Observation 3c90db96-a34f-4763-a788-d4ebaab9d654 · outbound

This paper cites Improving language models by retrieving from trillions of tokens,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Improving language models by retrieving from trillions of tokens,

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.457321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.709621Z digest=sha256:8223eaad0a56121290660869a6181f10b887cca117aa4a11785b0ba429a472fb

Observation ec1b7349-14cb-4d9d-94eb-b7ec208e95d2 · outbound

This paper cites Atlas: Few-shot learning with retrieval augmented language models,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Atlas: Few-shot learning with retrieval augmented language models,

Reference 4

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raw_fallback, observed 2026-08-11T11:30:14.440472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.714862Z digest=sha256:9070bec1026d869082138c5f4e864a892919f91f32941f3c1708c090261e87e9

Observation 55f5650f-59fa-44cd-846f-59af8c971b84 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Retrieval- augmented generation for knowledge-intensive nlp tasks,

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.720038Z digest=sha256:6bc6b0988d6010fb07813326e62ac6276d55552241249b9ccb064d059dca4945

Observation 3c6a9bd0-3c4e-40b2-aa69-8424484b0d2f · outbound

This paper cites Generalization through Memorization: Nearest Neighbor Language Models.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Generalization through Memorization: Nearest Neighbor Language Models

Reference 6

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no resolver link, observed 2026-08-11T11:30:13.725097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.725097Z digest=sha256:6f7b9d61a2d0902c04d2c0348e4ffaafec0027e8967875dc403dbff68d5fc37b

Observation 259ccb4c-0a95-4a78-a581-c588a607aa05 · outbound

This paper cites Retrieval-Augmented Multimodal Language Modeling.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Retrieval-Augmented Multimodal Language Modeling

Reference 7

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unresolved
no resolver link, observed 2026-08-11T11:30:13.730797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.730797Z digest=sha256:0644aa9d85d8f6ffddf4c1ac422416986048ae7853a6c10e8a70c12d600a87a8

Observation 324b3d1e-32f0-46d5-af59-df79a6a2eaec · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 8

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no resolver link, observed 2026-08-11T11:30:13.735536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.735536Z digest=sha256:361e76ed6d04ee120fa5bbdf5e4b853727ec999d9cf5e1a4e393d2e60adc8c43

Observation 789f776f-22be-477e-9339-ed205286b253 · outbound

This paper cites Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications for centralized and federated learning,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications for centralized and federated learning,

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.394967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.740424Z digest=sha256:ad3763f8dd91ef0973dc23304ad3ce0bcd6d22196a2a95d0a4247dfb540fab6f

Observation 8b61ad6b-befd-48cd-a54f-a4d614c5ea03 · outbound

This paper cites F-bids: Federated-blending based intrusion detection system,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection F-bids: Federated-blending based intrusion detection system,

Reference 10

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raw_fallback, observed 2026-08-11T11:30:14.376169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.745726Z digest=sha256:d929a9029b36a40f442b761830db07647206cbc0e71c2ed95b7429027a91cc8f

Observation b32d3156-2b7e-4c07-83e8-441b162df2ac · outbound

This paper cites An automatic and efficient malware traffic classification method for secure internet of things,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection An automatic and efficient malware traffic classification method for secure internet of things,

Reference 11

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raw_fallback, observed 2026-08-11T11:30:14.358714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.751124Z digest=sha256:d25a3b8b8793c27df2dd40550deb4e44152c053f9b3061312f3838b1408d19e7

Observation d2101d52-91a2-4c36-90fe-9b74e1a492dd · outbound

This paper cites 2df-ids: Decentralized and differentially private federated learning-based intrusion detection system for industrial iot,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection 2df-ids: Decentralized and differentially private federated learning-based intrusion detection system for industrial iot,

Reference 12

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raw_fallback, observed 2026-08-11T11:30:14.341848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.756318Z digest=sha256:20cfba1a10ae6c3e4b6040fa7ac74b24748142b719f13699ad486fdbcfc88f1a

Observation 26799f4c-9d9a-4949-a868-1d80e2153c6d · outbound

This paper cites A deep learning integrated blockchain framework for securing industrial iot,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection A deep learning integrated blockchain framework for securing industrial iot,

Reference 13

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raw_fallback, observed 2026-08-11T11:30:14.319933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.761164Z digest=sha256:f432b5ddb3a67769112f41d929e4c190205b0ebb1584567da2c61c2320af4c3e

Observation 7fe7be24-10dd-4f0b-ace9-8a03c2c71a61 · outbound

This paper cites Felids: Federated learning-based intrusion detection system for agricultural internet of things,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Felids: Federated learning-based intrusion detection system for agricultural internet of things,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.299473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.765718Z digest=sha256:f3847c5074bb09b33859cfee49ef04048313381402202324fe948c5c923cacc8

Observation 162b447e-8b5d-4d4f-be30-59c58fac461d · outbound

This paper cites Deepak-iot: An effective deep learning model for cyberattack detection in iot networks,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Deepak-iot: An effective deep learning model for cyberattack detection in iot networks,

Reference 15

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raw_fallback, observed 2026-08-11T11:30:14.282970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.770656Z digest=sha256:9c2edf8b57ebb2883ae0ec40e370a032935ab4cb1444273cd5917d920f0ee698

Observation 8ee45219-4229-490a-b839-48b1bac5b174 · outbound

This paper cites A hybrid cnn- lstm model for iiot edge privacy-aware intrusion detection,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection A hybrid cnn- lstm model for iiot edge privacy-aware intrusion detection,

Reference 16

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raw_fallback, observed 2026-08-11T11:30:14.266752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.775800Z digest=sha256:5e189459783dba831c49646f7778d3d1dd76df89a3cd566482d6d16a59ea2079

Observation 921c820b-c9ff-4b9a-be00-7bdeca2389f6 · outbound

This paper cites Generative ai for cyber threat-hunting in 6g-enabled iot networks,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Generative ai for cyber threat-hunting in 6g-enabled iot networks,

Reference 17

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raw_fallback, observed 2026-08-11T11:30:14.250105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.780748Z digest=sha256:a288bf476e83c1675e48ae9eec738e9b557f72ede485a667ec1264f186489df0

Observation c7741b22-d53c-4b69-8cee-e1d6d0ea662f · outbound

This paper cites Revolutionizing cyber threat detection with large language models: A privacy-preserving bert- based lightweight model for iot/iiot devices,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Revolutionizing cyber threat detection with large language models: A privacy-preserving bert- based lightweight model for iot/iiot devices,

Reference 18

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raw_fallback, observed 2026-08-11T11:30:14.232795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.785360Z digest=sha256:f7e4dae25ec82ae1378578bc08a834bc64bbc1799c1909a7756e099ebce908c8

Observation 6f64a462-cba6-405b-8db0-d699e506131a · outbound

This paper cites Iot-llm: Enhancing real- world iot task reasoning with large language models,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Iot-llm: Enhancing real- world iot task reasoning with large language models,

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.790981Z digest=sha256:cc0797fd7d9525f06fd594bd90fabb834b198a6b26d7436ceb43f319fb2140ef

Observation 23d997f4-7278-4ccf-8ca5-237d507bbbb7 · outbound

This paper cites Penetrative ai: Making llms comprehend the physical world,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Penetrative ai: Making llms comprehend the physical world,

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.795589Z digest=sha256:0c70bc20a361e2ac69f4cb129d59cc1ca0761ca8a6a0fa84d5ca738eccdfbf22

Observation 5390954d-e5b9-45a3-89dc-4b8c6589d8a3 · outbound

This paper cites IoT-LM: Large Multisensory Language Models for the Internet of Things.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection IoT-LM: Large Multisensory Language Models for the Internet of Things

Reference 21

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no resolver link, observed 2026-08-11T11:30:13.800029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.800029Z digest=sha256:91315ef4c22bd238926ae04b460e2f947b51a93c6236e470bc062b988a07ecea

Observation 1589ea16-d55e-40f6-94e1-555bed5a89a9 · outbound

This paper cites Efficient Prompting for LLM-based Generative Internet of Things.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Efficient Prompting for LLM-based Generative Internet of Things

Reference 22

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no resolver link, observed 2026-08-11T11:30:13.805537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.805537Z digest=sha256:02d6b05b54ec4d76d5a6a6061998f5722b537bbd8d0872b0972dcd92a5cf6d68

Observation ca1d02ff-796a-4e5e-aa19-374bdae07624 · outbound

This paper cites When IoT Meet LLMs: Applications and Challenges.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection When IoT Meet LLMs: Applications and Challenges

Reference 23

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no resolver link, observed 2026-08-11T11:30:13.810621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.810621Z digest=sha256:5fc7ca517681fc7be633d972a917463334fbee137e5055e5c891da797eebd51a

Observation febf3397-6652-4ee5-951a-15a888ab2ab4 · outbound

This paper cites Pllm-cs: Pre-trained large language model (llm) for cyber threat detection in satellite networks,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Pllm-cs: Pre-trained large language model (llm) for cyber threat detection in satellite networks,

Reference 24

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raw_fallback, observed 2026-08-11T11:30:14.204728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.815887Z digest=sha256:f92ea5179b3fa2f25b56a32c2a5003977b87520653034871f70595240a7fba0f

Observation 3ffb8b27-ad62-4c18-bdf0-02cfbedaaf71 · outbound

This paper cites Detecting command injection vulnerabilities in linux- based embedded firmware with llm-based taint analysis of library functions,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Detecting command injection vulnerabilities in linux- based embedded firmware with llm-based taint analysis of library functions,

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.187632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.821358Z digest=sha256:91166b22bbad5b52109bf2d5a233be443879cf549daabea375a1e1ac10419c60

Observation 8e189384-342d-4a96-a9b6-63f4f65d0165 · outbound

This paper cites Can- bert do it? controller area network intrusion detection system based on bert language model,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Can- bert do it? controller area network intrusion detection system based on bert language model,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.172053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.825897Z digest=sha256:26902f699f974a08d6340e1d58cfa09b755147289d0a9aba1ccc4848cf929f0a

Observation aa0fbc79-07c3-47b2-9259-dd993f117ec9 · outbound

This paper cites Malbert: Malware detection using bidirectional encoder representations from transformers,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Malbert: Malware detection using bidirectional encoder representations from transformers,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.157237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.830506Z digest=sha256:cc0bd1aa6c0bf7b9715c97357c54ddbc7294da2dfbf2c4986f376f0be8d56093

Observation b02bd977-9de3-428c-9abe-1f1733ab3936 · outbound

This paper cites Bert-log: Anomaly detection for system logs based on pre-trained language model,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Bert-log: Anomaly detection for system logs based on pre-trained language model,

Reference 28

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no resolver link, observed 2026-08-11T11:30:13.838951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:13.838951Z digest=sha256:6cd79c144ed4f546ae208906a59a6b22abb9414ed51f24e5851b4c005e03c5dd

Observation 7368f821-d0d2-4061-a1b6-c691e39fec6e · outbound

This paper cites Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-08-11T11:30:13.908371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.845222Z digest=sha256:b686818e4651d28dddcf7ac727694fa3ae3a6feae3fd93b7565781d117ba1e81

Observation 8a99a3ae-cc86-4c44-8de0-a43fc8e7416a · outbound

This paper cites Pac-gpt: A novel approach to generating synthetic network traffic with gpt-3,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Pac-gpt: A novel approach to generating synthetic network traffic with gpt-3,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.133174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.850590Z digest=sha256:22f836007b2cf10459099dc3b14c99b5f652010684ffd62d842f2540183bd271

Observation 0ff35a7d-7bd8-4423-82fb-4367eeb2e6bd · outbound

This paper cites Cybert: Contextu- alized embeddings for the cybersecurity domain,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection Cybert: Contextu- alized embeddings for the cybersecurity domain,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.117566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.855925Z digest=sha256:71877f4082daeb05da42e7b7548cb54d44407f756759b7c2ba5bb60cd361bdf9

Observation 40409ca5-069a-4fe8-ad5a-09e9e3803046 · outbound

This paper cites A lightweight iot intrusion detection model based on improved bert-of-theseus,.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection A lightweight iot intrusion detection model based on improved bert-of-theseus,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:30:14.101739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T11:30:13.860530Z digest=sha256:c28e3faac6b268728280a4a169bcca9f3e62ae127a9d86a54902ee03f39ff12b

Pith citing papers

Observation 39260d82-b304-44bc-8a72-f4250e0e2008 · inbound

LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems cites this paper.

LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-16T04:50:59.948178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T04:50:59.882257Z digest=sha256:5f39535991b263c0d8823a4363522c5e65bb8232b205ec07d3137de2064394ed