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Paper Citation Record · LEDGER

Evaluating Language Models For Threat Detection in IoT Security Logs

As of 20 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2507.02390.

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

pith.paper-citation-record.v1
2507.02390 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:36:44.183864Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T22:00:18.908449Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

  • verified exact2
  • verified fuzzy27
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e5000411-e224-41b1-a025-789e82705f46 · outbound

This paper cites Detecting large-scale system problems by mining console logs,.

Evaluating Language Models For Threat Detection in IoT Security Logs Detecting large-scale system problems by mining console logs,

Reference 1

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raw_fallback, observed 2026-08-06T20:36:49.748922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.026523Z digest=sha256:e4ffd16f70afa5bdf8eb99a61d7de94d6ec59678e8876daaebd7e39d0340f0c7

Observation b26cd5b6-6492-443e-b5cd-c96fcf346b42 · outbound

This paper cites Isolation forest,.

Evaluating Language Models For Threat Detection in IoT Security Logs Isolation forest,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-06T20:36:49.599633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.065293Z digest=sha256:7bd01a1b5e179d805e802efdeafb6cda0d4daa15d59cf9ee6bfd0f6429ff51e1

Observation 8e30620b-a020-4997-88da-f899c0f3d801 · outbound

This paper cites Anomaly intrusion detection using one class svm,.

Evaluating Language Models For Threat Detection in IoT Security Logs Anomaly intrusion detection using one class svm,

Reference 3

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raw_fallback, observed 2026-08-06T20:36:49.457960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.086496Z digest=sha256:267cd1ac6f590f67b107df91bb3f8882db307a52ed34973253ed1d6354347fdd

Observation fe65ba14-3591-4fa5-ab17-fa1b21273505 · outbound

This paper cites LogGPT: Log anomaly detection via GPT,.

Evaluating Language Models For Threat Detection in IoT Security Logs LogGPT: Log anomaly detection via GPT,

Reference 4

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raw_fallback, observed 2026-08-06T20:36:49.233111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.123474Z digest=sha256:872c92e2e2f160035e6c81ccfe81ea0402a9abaf1518942cab828e1c03a0eaef

Observation 2f90fce9-e3eb-464c-8bb3-6c9a70ef6551 · outbound

This paper cites LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis.

Evaluating Language Models For Threat Detection in IoT Security Logs LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis

Reference 5

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local_arxiv, observed 2026-08-06T20:36:44.834631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.201440Z digest=sha256:bc41b22cff32f9e75c6d571aaffdce562d70b42acfe3c089e7b0aff3c774da1b

Observation 839c1276-78cd-4a53-8d90-6272e1c631ab · outbound

This paper cites LLM-based event log analysis techniques: A survey.

Evaluating Language Models For Threat Detection in IoT Security Logs LLM-based event log analysis techniques: A survey

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T20:36:41.305365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:36:41.305365Z digest=sha256:e234c6789147f90f44eb97173106f6d113ce56b5c81f8645153405c15b47df73

Observation f78506fd-9a05-420d-9b16-b0a4456f5f7e · outbound

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

Evaluating Language Models For Threat Detection in IoT Security Logs Revolutionizing cyber threat detection with large language models: A privacy-preserving BERT-based lightweight model for IoT/IIoT devices,

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-06T20:36:49.065654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.365106Z digest=sha256:d91d11c680dd6c440f6cabbfc94c43734ecdc0dbb3205d9d1de6ab34fb7c77a4

Observation 161679b0-9e1c-4b45-871a-702e1cff029c · outbound

This paper cites Ton_iot datasets,.

Evaluating Language Models For Threat Detection in IoT Security Logs Ton_iot datasets,

Reference 8

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verified fuzzy
raw_fallback, observed 2026-08-06T20:36:48.869804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.410749Z digest=sha256:95512cc559d24d8a6d8a36ceea98236e826b748763b538ae531c5bf7ee1a604e

Observation 95a88b7a-376f-4281-96b0-54f94b3e9189 · outbound

This paper cites Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset,.

Evaluating Language Models For Threat Detection in IoT Security Logs Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset,

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-06T20:36:48.624006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.470005Z digest=sha256:2ff839d16ebf3c845cfaae33fa6429f26449687719bf6e813e703725fae535a8

Observation 5cac4261-838b-47e5-8a6c-df483bd0fb95 · outbound

This paper cites CLDTLog: System log anomaly detection method based on contrastive learning and dual objective tasks,.

Evaluating Language Models For Threat Detection in IoT Security Logs CLDTLog: System log anomaly detection method based on contrastive learning and dual objective tasks,

Reference 10

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raw_fallback, observed 2026-08-06T20:36:48.480245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.515082Z digest=sha256:0b1afa6cbc24ad35c497c653952a411fce024789d62ec1a8ce20a85b480feeb3

Observation facfb5a8-549e-42dc-89e9-194608fcf2a8 · outbound

This paper cites LogBERT: Log anomaly detection via BERT,.

Evaluating Language Models For Threat Detection in IoT Security Logs LogBERT: Log anomaly detection via BERT,

Reference 11

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raw_fallback, observed 2026-08-06T20:36:48.279917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.568316Z digest=sha256:bb61c98ddfab20e6569c752f35808f1c944810daf5c89636d4ec6faf4c67c14f

Observation cc7d2938-a5bb-4611-868a-65b59f83cbaa · outbound

This paper cites Deeplog: Anomaly detection and diagnosis from system logs through deep learning,.

Evaluating Language Models For Threat Detection in IoT Security Logs Deeplog: Anomaly detection and diagnosis from system logs through deep learning,

Reference 12

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unresolved
no resolver link, observed 2026-08-06T20:36:41.653564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:36:41.653564Z digest=sha256:8d81c2228c935b66e733fca58943471c4db45f3ef860f1f8ffe9d18be7505136

Observation 6ffddb81-abde-4ca1-ac6e-da44cf163a6d · outbound

This paper cites Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs,.

Evaluating Language Models For Threat Detection in IoT Security Logs Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs,

Reference 13

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raw_fallback, observed 2026-08-06T20:36:48.102084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.717156Z digest=sha256:ecd8b3e01096fb4ead6f4adeebeaf45ded0869ee9fa28bae9aa09e7c624b4d41

Observation 1e3ff5df-3a01-4019-a4aa-be47212201bb · outbound

This paper cites What supercomputers say: A study of five system logs,.

Evaluating Language Models For Threat Detection in IoT Security Logs What supercomputers say: A study of five system logs,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-06T20:36:47.866653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.803926Z digest=sha256:20773da151f9fac26906ef1d92e960ef995c1ae4ed2f712daea956e0ed733d91

Observation 6b296f71-0367-451a-b2c0-85c2980b4a33 · outbound

This paper cites LLM meets ML: Data-efficient anomaly detection on unseen unstable logs,.

Evaluating Language Models For Threat Detection in IoT Security Logs LLM meets ML: Data-efficient anomaly detection on unseen unstable logs,

Reference 15

Resolution
verified exact
raw_fallback, observed 2026-08-06T20:36:44.588954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.839496Z digest=sha256:5d759b06e2bc48ccf121027c527326e1de58a1db419c5603cd074fa9485d6eca

Observation e746778b-c455-47d0-926a-9861319f3386 · outbound

This paper cites Fine-tuning llms vs non-generative machine learning models: A comparative study of malware detection,.

Evaluating Language Models For Threat Detection in IoT Security Logs Fine-tuning llms vs non-generative machine learning models: A comparative study of malware detection,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:36:47.665627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.919136Z digest=sha256:6b060e14f795da90dd229ca3ad6915c2ba59667fa18715e07d4b6bc1398f1fc1

Observation 5e1e4a8c-2474-41c6-9867-c42d5a964a52 · outbound

This paper cites Iot-23 dataset,.

Evaluating Language Models For Threat Detection in IoT Security Logs Iot-23 dataset,

Reference 17

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raw_fallback, observed 2026-08-06T20:36:47.411649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:41.997604Z digest=sha256:fea705dad18140e53a87b7f6513707043b300dcb69bae6020dbaee0b8a809e8f

Observation 8b1b4648-a00b-413e-aff2-58919ef42680 · outbound

This paper cites Cic datasets,.

Evaluating Language Models For Threat Detection in IoT Security Logs Cic datasets,

Reference 18

Resolution
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raw_fallback, observed 2026-08-06T20:36:47.218278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:42.081743Z digest=sha256:1724240b6bcbb3415d611ef3c2af882a684d81a3427c9c63d81cf385c6a3033f

Observation ebee275d-8799-4c2d-ae84-77cabdb32026 · outbound

This paper cites Rt-iot 2022: Real-time internet of things dataset,.

Evaluating Language Models For Threat Detection in IoT Security Logs Rt-iot 2022: Real-time internet of things dataset,

Reference 19

Resolution
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raw_fallback, observed 2026-08-06T20:36:47.005416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:42.202042Z digest=sha256:ffc6a09676c99f58380d2a82018a7d4e54f3cf135e3753e6fb298282a4e23731

Observation 2053af5c-ffc7-465e-9115-5aa58c25cb4b · outbound

This paper cites Edge-iiotset: Cyber security dataset of iot & iiot,.

Evaluating Language Models For Threat Detection in IoT Security Logs Edge-iiotset: Cyber security dataset of iot & iiot,

Reference 20

Resolution
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raw_fallback, observed 2026-08-06T20:36:46.802639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:42.318390Z digest=sha256:478c5527db092b0f12519154275dc9a1f9a14e391b812783fddae8ed8c495c1d

Observation 804ac058-5ce9-4bdc-9a6f-02a9fcdf47ce · outbound

This paper cites Application of large language models to DDoS attack detection,.

Evaluating Language Models For Threat Detection in IoT Security Logs Application of large language models to DDoS attack detection,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:36:46.675541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:42.358066Z digest=sha256:697f5647713c1a1a45c5d487ba5cb0e1651c965a1bcc0ab938856d6de2cefc7a

Observation 4d806028-10c0-4cbc-a2a4-e58cb3105992 · outbound

This paper cites HackMentor: Fine-tuning large language models for cybersecurity,.

Evaluating Language Models For Threat Detection in IoT Security Logs HackMentor: Fine-tuning large language models for cybersecurity,

Reference 22

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raw_fallback, observed 2026-08-06T20:36:46.528439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:42.429969Z digest=sha256:bb685edd1c1f694c0357d4d3700626910c8fd7802261165f79c955d175287fda

Observation 8619a6d1-3c12-4591-b458-4c612d58c4ea · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Evaluating Language Models For Threat Detection in IoT Security Logs LoRA: Low-Rank Adaptation of Large Language Models

Reference 23

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no resolver link, observed 2026-08-06T20:36:42.538502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:36:42.538502Z digest=sha256:f1b8398bcda05a01151cdd54e2c310a95fb93430ae0f28f334d3050e27f28af2

Observation c5f00f0a-0c63-4ec8-9887-2fd639ad6883 · outbound

This paper cites LLM4itd: Insider threat detection with fine-tuned large language models,.

Evaluating Language Models For Threat Detection in IoT Security Logs LLM4itd: Insider threat detection with fine-tuned large language models,

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-06T20:36:46.310273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:42.580194Z digest=sha256:78df70340d94d8776443419e24bd8341e410a539d5b07a4545e235047fb37bbc

Observation a13e63dd-a8c5-4588-a519-16db4fbac606 · outbound

This paper cites Building cyber language models to unlock new cybersecurity capabilities.

Evaluating Language Models For Threat Detection in IoT Security Logs Building cyber language models to unlock new cybersecurity capabilities

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:36:46.197065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:42.694071Z digest=sha256:c6bb37c153979a5441951c40895169bf0770a51e19985735bdd6bab3cab364b9

Observation 6c581a9e-7bd8-4eb8-9e18-f4cbe19b6c6b · outbound

This paper cites Benchmarking large language models for log analysis, security, and interpretation,.

Evaluating Language Models For Threat Detection in IoT Security Logs Benchmarking large language models for log analysis, security, and interpretation,

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T20:36:42.803180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:36:42.803180Z digest=sha256:6b7b63eda1762fbe6f1f865a330c3427e58f71bd250f3b1633bdc061b99710ea

Observation a48b6389-5cfe-4fa4-a36e-58a8dcaa2a18 · outbound

This paper cites Building a dynamic parserless network log and security alert platform with LLM and pydantic.

Evaluating Language Models For Threat Detection in IoT Security Logs Building a dynamic parserless network log and security alert platform with LLM and pydantic

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:36:46.066425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:42.879189Z digest=sha256:1dd593dfdffc68aad9f4cdf2941dbd01509bcbb0f102d39ddbdd3f3e831edb31

Observation 57fc48e7-ff51-4d07-a978-add433d28ae1 · outbound

This paper cites Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models.

Evaluating Language Models For Threat Detection in IoT Security Logs Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models

Reference 28

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unresolved
no resolver link, observed 2026-08-06T20:36:42.968855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:36:42.968855Z digest=sha256:78a4f1f9ea7d7b257671cd9d799ce47ef5979530d273016c327c57e4cdb62029

Observation b57d888a-8d14-4880-b2ca-167ff14526ff · outbound

This paper cites RedChronos: A Large Language Model-Based Log Analysis System for Insider Threat Detection in Enterprises.

Evaluating Language Models For Threat Detection in IoT Security Logs RedChronos: A Large Language Model-Based Log Analysis System for Insider Threat Detection in Enterprises

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T20:36:43.014195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:36:43.014195Z digest=sha256:c8c537418975dbee98ff98cfead7f0cee6aca434e0b4d141fe73b9d7b143e7f5

Observation d09e6250-abde-43ef-95c0-9d1f3cfa3827 · outbound

This paper cites LogPrécis: Unleashing language models for automated malicious log analysis: Précis: A concise summary of essential points, statements, or facts,.

Evaluating Language Models For Threat Detection in IoT Security Logs LogPrécis: Unleashing language models for automated malicious log analysis: Précis: A concise summary of essential points, statements, or facts,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:36:45.892751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:43.179070Z digest=sha256:06c412cab3a63b88e5f3038a5043e6e8dbc205df41747f009552822b7c25c98d

Observation 256e022e-133f-439c-9977-4d49d4711e04 · outbound

This paper cites Mitre att&ck labeling of cyber threat intelligence via llm,.

Evaluating Language Models For Threat Detection in IoT Security Logs Mitre att&ck labeling of cyber threat intelligence via llm,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:36:45.740852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:43.292296Z digest=sha256:c287808afed6c8bbac1286f1d9dc81312e21013748e683a8ca85d89459ff9b93

Observation 19f47f58-8d14-491a-844a-74be582f1cdd · outbound

This paper cites When llms meet cybersecurity: A systematic literature review,.

Evaluating Language Models For Threat Detection in IoT Security Logs When llms meet cybersecurity: A systematic literature review,

Reference 32

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unresolved
no resolver link, observed 2026-08-06T20:36:43.401357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:36:43.401357Z digest=sha256:2eccba88e600bcf4a8bc9016a0c96103208e9829ed1d139a68d2fa68226a7c98

Observation 966cee1a-6967-4fb9-91eb-d2151d3b500d · outbound

This paper cites Capec: Common attack pattern enumeration and classification,.

Evaluating Language Models For Threat Detection in IoT Security Logs Capec: Common attack pattern enumeration and classification,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:36:45.648027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:43.543938Z digest=sha256:be70447bc5d2e8fc4042e9a24203975e1d210cf105773fd04cef272cc356391b

Observation 0f25ab64-cc9f-4701-a904-4532cf06a5a7 · outbound

This paper cites Transformers: State-of-the-art machine learning for pytorch, tensorflow, and jax,.

Evaluating Language Models For Threat Detection in IoT Security Logs Transformers: State-of-the-art machine learning for pytorch, tensorflow, and jax,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:36:45.560297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:43.668746Z digest=sha256:b74a54aa4035c93e2218f023725411cae637502470e8bb90355d08f244767258

Observation bf186444-148f-4f26-9143-29b2f2c6d492 · outbound

This paper cites Unsloth,.

Evaluating Language Models For Threat Detection in IoT Security Logs Unsloth,

Reference 35

Resolution
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raw_fallback, observed 2026-08-06T20:36:45.375590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:43.809677Z digest=sha256:bbe1e5f34be12430b9dc10af21f886f280a528d0ba51f24462a1db14cc37fa67

Observation e00e4e78-330b-478e-bdb9-54ecaf1ce523 · outbound

This paper cites Deepseek,.

Evaluating Language Models For Threat Detection in IoT Security Logs Deepseek,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:36:45.287061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:43.931763Z digest=sha256:dce839d7103ea355d3585ce74f4beb67380dfb476ee993064765beb1919297bb

Observation 41a52390-29b2-4cd1-84c1-a9596823626b · outbound

This paper cites an unresolved cited work.

Evaluating Language Models For Threat Detection in IoT Security Logs Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:36:45.187745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:44.062566Z digest=sha256:2ef8905e9294a2106f3a2a19e5bd2e22dd90e156223d9324b9e604ea866c3262

Observation 9b37c21b-cf99-4c76-bbca-82d2b3ffcfed · outbound

This paper cites an unresolved cited work.

Evaluating Language Models For Threat Detection in IoT Security Logs Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:36:45.046496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T20:36:44.183864Z digest=sha256:e7773fb491235edd863a4d7dcdde902a8d04e1f85c35ca02210d5bd5b54cd73a

Pith citing papers

Observation 10bfe175-6308-4cfd-a4b6-cba4d8b76f50 · inbound

Just Testing, Move Along: Evasion of LLM-based System Log Interpretation by Prompt Injection cites this paper.

Just Testing, Move Along: Evasion of LLM-based System Log Interpretation by Prompt Injection Evaluating Language Models For Threat Detection in IoT Security Logs

Reference 35

Resolution
unresolved
no resolver link, observed 2026-07-31T22:00:18.908449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T22:00:18.908449Z digest=sha256:eee46cc5b28342eea5e2d6d46fd4e69cd25420e58855b33bba6abaa6bd4b36ab