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Paper Citation Record · LEDGER

Evaluating Language Models For Threat Detection in IoT Security Logs

As of 17 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 2026-08-17T06:30:58.91139+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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verified fuzzy
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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.026523Z digest=sha256:2c60de02518a3ecdd065d8ada11c3210145823dfc2d1336cdb3a6c175ad5a679

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.065293Z digest=sha256:8b1277f4e4f5005949260a18398ee614c4420af08e19898ae9d88b723c0986b7

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.086496Z digest=sha256:f2719188b04a87e9dd437de2a14aaee610e09d86ffc24e0e2a42ed040dbd9bdb

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.123474Z digest=sha256:468f2dc734b78e9872f1cec8d890dccd364e9755df18337c24c3e88e87729797

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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verified exact
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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.201440Z digest=sha256:a3ca303b4c2b68e8b1eef2ad6b875b487ce619aa14ea75206a8d2d1dc530ae07

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=sha256:61d7f88715dabc60ceb154956416c593dddaf443effdd09960789594e16da88a

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.365106Z digest=sha256:c32fec886262cb3e5b0bf4b0f425dc8db5c128138e0cda82de9e9e848ca0711c

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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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.410749Z digest=sha256:54ce1041a73184dcaaba1a39c28d54e09dd53e07742ecb848961a7bead6c78ae

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.470005Z digest=sha256:f0e8b19c3acbaddf90a870f06e8300a75c082183f7cef43e8c94aea6ecdd51ff

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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verified fuzzy
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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.515082Z digest=sha256:cf4829e13402603037bd2fa82159f58975ead3c922691959e26c293d8beb4f9e

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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verified fuzzy
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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.568316Z digest=sha256:d49bd89c9f0ba582c625cfecaff420bd11ed308b6388f31aa374b4bc662b47ea

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

Resolution
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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.717156Z digest=sha256:d38d1d9e74abd3be124fbe9ef8fe0229a72b0bd7d50ddab872bd53b372ba1241

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

Resolution
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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.803926Z digest=sha256:5d56f3d50bf325d4dcb9851329f84c57805765ba29d35999e0052eda1797149f

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.839496Z digest=sha256:8048a276a392b61d96e9170e940d988370f636f515ce33373b4841bc1b9df4f3

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.919136Z digest=sha256:1817a8d0cb30c2f35abd6f0fed47da1a15cea871587dbda4780644792b93a438

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:41.997604Z digest=sha256:00d9d183c410fb4de16eb7ed3bdcbff511e96f4ff791bcdae04458cb0d62fd55

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:42.081743Z digest=sha256:74a8a869172979db47268cf3b80d5764a96bf297d7afb3479f087d88e435d1ae

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:42.202042Z digest=sha256:73edf87c84c332f39f7c099da517bd3737f6c6f1c260f5a634fcc53375f1dfb1

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

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verified fuzzy
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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:42.318390Z digest=sha256:2aba7bddf2bda7646f49e741451d7ab2f83e52a347904ae6b8a2d4c6aabb06eb

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

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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:42.358066Z digest=sha256:0dfbe3cc51c736e3ba5e12528fd9881ade91fcddd18a6917b8f06e61ca74ca4c

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:42.429969Z digest=sha256:510177558fdbcd8ea89dec3e5cc98b1bd32270f1dc976414991f79b7db5932c7

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=sha256:9fbd2fd3fcb0322c607afc58dba4dbe3849de519129ddff6915bef327979484c

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:42.580194Z digest=sha256:8ae1c6368e524a8591e13832311e84412e195de493afa14fe3bca10cda57b082

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:42.694071Z digest=sha256:56542824a47919ed482249fec1effbf4bae8cd229625395ca2c3ff582c527709

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

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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:42.879189Z digest=sha256:207ab1ca755d655584da085d6177a1016383a5aea7427df709261d7cd1af6fe8

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

Resolution
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=sha256:3abdf0891c27b4bda5b94430984ebd26c3c5e3070673197280c6a71c971b1d1c

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:43.179070Z digest=sha256:a07f791d22dcc6b5cc9e04b3d1c4b9fd4dcd34f68f26e22fd3cdad5b49c864c1

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:43.292296Z digest=sha256:21a341ffa10c549834ed0d32913ed7e40c11d9a390aeb000bb1a159867157b66

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:43.543938Z digest=sha256:2d2434a1c20a938059f4b1765a7c58967d5653180d20cfccd947e27bc71021a9

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:43.668746Z digest=sha256:e87765b276cba99433cd6fd6c51dbfa130012aa89990b6260fe34f1f9118738e

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
verified fuzzy
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 2026-08-17T06:30:58.91139+00:00.

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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 2026-08-17T06:30:58.91139+00:00.

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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:44.062566Z digest=sha256:e52caf32bbcb6ca3e5b49e03642e818399afd1919d2b6c04a3a6434848741964

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:36:44.183864Z digest=sha256:7855dd7c9068caf324c7839c1f0e9c01ce58b43e333c97df08bda86443054405

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