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Paper Citation Record · LEDGER

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks

As of 21 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2607.09659.

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

pith.paper-citation-record.v1
2607.09659 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T01:24:25.864311Z

measured 48 of 48 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 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

48 of 48 outbound references displayed

  • verified exact11
  • verified fuzzy0
  • unresolved37
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cf963950-eef9-405f-be8c-ece4861816e6 · outbound

This paper cites Encrypted network traffic analysis and classification utilizing machine learning.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Encrypted network traffic analysis and classification utilizing machine learning

Reference 1

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doi, observed 2026-07-13T01:29:15.961879Z

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

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:b359bc9cd251fc821b6ed89a08b7b7ea72ae07fff61f479ff87b625e461970cf

Observation efbf2a25-bde5-4dfd-8b2a-ee488c5fb993 · outbound

This paper cites Monitoring encrypted communication with opc ua.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Monitoring encrypted communication with opc ua

Reference 2

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:a5ba426937db7ed457449ca498f8d5577491a9494f16a7e0e1ffc73731968055

Observation 064417cf-2e16-47b0-9b33-591167534107 · outbound

This paper cites An experimental study of machine learning-based intrusion detection for opc ua over industrial private 5g networks.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks An experimental study of machine learning-based intrusion detection for opc ua over industrial private 5g networks

Reference 4

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arxiv_id, observed 2026-07-13T01:29:15.942053Z

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:92e41bda49c160d5804ca36508e4e8f24bf1eb1c1e827c57a5b3ea91ab2ef447

Observation 35c30a71-b01e-4eb5-8a8e-3a7769562140 · outbound

This paper cites Unsupervised graph-sequence anomaly detection for 5g core network control plane traffic.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Unsupervised graph-sequence anomaly detection for 5g core network control plane traffic

Reference 6

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:a7faaf3c915ee744aa6239acf90136a667176cc5bd7a8f1f37d064132cb4825a

Observation 56f10405-d6cd-42ea-b775-5b841a58793f · outbound

This paper cites Shyaa, Noor Farizah Ibrahim, Zurinahni Zainol, Rosni Abdullah, Mohammed Anbar, and Laith Alzubaidi.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Shyaa, Noor Farizah Ibrahim, Zurinahni Zainol, Rosni Abdullah, Mohammed Anbar, and Laith Alzubaidi

Reference 7

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arxiv_id, observed 2026-07-13T01:29:15.951064Z

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=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:d3d73384da79e1b6e0c4f1536332c62e01e11c9c491a385c2338f83a660da45f

Observation e61dea1e-ef54-44fe-9a6c-35229559047f · outbound

This paper cites Testbed and Software Architecture for Enhancing Security in Industrial Private 5G Networks.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Testbed and Software Architecture for Enhancing Security in Industrial Private 5G Networks

Reference 9

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:2d4c5d0fca1a9ea9cb985741a8189bb5fea231a2405dd62fb80cbf9c683aaae7

Observation d0a24893-a6af-4d07-a725-5b5a2b599f62 · outbound

This paper cites Opc-ua exploitation framework.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Opc-ua exploitation framework

Reference 10

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:1dc1d801810e80b2ec9c2a32ee113606ca101dcd2c4f364d8fffc02aa52e312c

Observation 5cbac39e-8b66-42ad-aba6-16aafacc000f · outbound

This paper cites Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning , volume =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning , volume =

Reference 11

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:c4cf856bd2e9d3b4cc3766c1a13c7b48289cd36fe908400d7f5059802d35a659

Observation c45c25df-5774-4bda-9439-689c4c970823 · outbound

This paper cites A survey on the handover management in 5G-NR cellular networks: aspects, approaches and challenges , volume =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks A survey on the handover management in 5G-NR cellular networks: aspects, approaches and challenges , volume =

Reference 12

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:4a2f501f0b027bcae9725a66daf7dad1cfd8184b13f03673f92d61844cb7eaf1

Observation 5b262ea8-1f29-438c-9555-988d34dff473 · outbound

This paper cites Electronics , VOLUME =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Electronics , VOLUME =

Reference 13

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:9954baa8a52b0a85e8d4dcede32aa5d82f0aea18697124ae5fa33dfbadbb3dca

Observation 797bc7d6-4e86-4989-a9fa-ccc6558063ea · outbound

This paper cites Using Anomaly Detection Techniques for Securing 5G Infrastructure and Applications , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Using Anomaly Detection Techniques for Securing 5G Infrastructure and Applications , year=

Reference 14

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:d4c73a6f5cc4a7096a25229a31c3b3b78e980016845f6f3eaf3a34f6450e0f1d

Observation 3ae14616-c9ab-4003-9488-137fc14482cc · outbound

This paper cites Alves and Mateus A.S.S.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Alves and Mateus A.S.S

Reference 15

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doi, observed 2026-07-13T01:29:15.971738Z

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=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:244d03ccb5e963cbe0181f7c2b42fe813eb678ea7eada28c9c39a148f2eecfe6

Observation 02394091-ccd6-4d0a-a105-e3534e507c22 · outbound

This paper cites and Macedo, Daniel F.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks and Macedo, Daniel F

Reference 16

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:5ad9b43f508e6bd63ca04c29a5cc0e92d086c71afb17aed2388bb28cfc3a82e0

Observation be4f8757-de61-4810-ab01-305711a61071 · outbound

This paper cites Sensors , VOLUME =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Sensors , VOLUME =

Reference 17

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:fa09bcf1ded6c7f179cc1073b75579060edefb47242b5c0f72f3b1a27c973909

Observation 9472982a-d4ee-47a6-9440-4a1e1609b292 · outbound

This paper cites Development of a monitoring system for encrypted data by OPC UA , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Development of a monitoring system for encrypted data by OPC UA , year=

Reference 18

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:7263d0f31b8430e1b14ae115a1078768a313c2800e1883794f5b857640bf7d8e

Observation 14af0ae5-9b27-4dc6-8dea-d7c959967d43 · outbound

This paper cites Big Data and Cognitive Computing , VOLUME =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Big Data and Cognitive Computing , VOLUME =

Reference 19

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:f431e67537482a7747f800538113c52110345be48217e953ade256c8e47fd11d

Observation 2f2adeac-b209-44e5-954c-4d816245fd5f · outbound

This paper cites and Radunovic, Bozidar , title =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks and Radunovic, Bozidar , title =

Reference 20

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:19abcbba9de9ba608a1f7ef1c245c983f64d1d854b89ba199891cbfcb0963bfb

Observation e0a855fd-6a00-4c29-a165-5f3236678f9e · outbound

This paper cites Descriptor: 5G Open Radio Access Network Multi-Modal Intrusion Detection Dataset (NetsLab-5GORAN-IDD) , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Descriptor: 5G Open Radio Access Network Multi-Modal Intrusion Detection Dataset (NetsLab-5GORAN-IDD) , year=

Reference 21

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:1f14f9c2b8aa51d5a1e6c47d33ac464e01af77ceb9d22490e01aff67f15736de

Observation 3e9f351a-62ad-43b8-84b6-4d05cc15da6e · outbound

This paper cites and Gan, Hongping , journal=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks and Gan, Hongping , journal=

Reference 22

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:95aeafe34edd29d087ae37b00d44090d0a4cf37cf8c77dfdeceaf025d29e1b1d

Observation 0b8acd36-967b-4ec7-9d9f-5f13e5cb4097 · outbound

This paper cites Dual-Branch Transformer for Anomaly-Based Intrusion Detection from Multivariate KPIs in the 5G User Plane , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Dual-Branch Transformer for Anomaly-Based Intrusion Detection from Multivariate KPIs in the 5G User Plane , year=

Reference 23

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:1d0483c005fe2d0b5af8e2aedfc30d62d9316792ec41fe4c92bfa3a258bb2286

Observation 6c0e3522-b382-429c-9f33-f9d437292392 · outbound

This paper cites Computer Modeling in Engineering & Sciences , VOLUME =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Computer Modeling in Engineering & Sciences , VOLUME =

Reference 24

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:47da276983ec67f3dcbb807477b65e7f3efdbb8a05c6658a0f604c723fd1bfb3

Observation e2f9e6fe-6b81-4b86-902c-b2edf91347cf · outbound

This paper cites 2025 , volume=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks 2025 , volume=

Reference 25

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:68625141306bfefd7241afac0f3df849b080fef7ca5a5b42f2936d7f33b0c9e4

Observation d7396cae-b953-4df8-b572-5cd9487806b8 · outbound

This paper cites 2023 , url =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks 2023 , url =

Reference 26

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:da3188d943512c72460af4f98e9d3a055283cccefc45252ca60b8d09c6e253b9

Observation fa3aa845-1208-4e9a-ac01-7a10b16f6617 · outbound

This paper cites Granomaly: A Framework for Anomaly Detection in 5G Core Network Control Plane Traffic with Temporal Graph Neural Networks , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Granomaly: A Framework for Anomaly Detection in 5G Core Network Control Plane Traffic with Temporal Graph Neural Networks , year=

Reference 27

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:6fe6fd9e5074e8909ddc5b64582aa8b79cd8a53c974d775cd50e65e420dce11a

Observation 3313a864-83fe-4dd2-b46b-5f638fd256dc · outbound

This paper cites ADSeq-5GCN: Anomaly Detection from Network Traffic Sequences in 5G Core Network Control Plane , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks ADSeq-5GCN: Anomaly Detection from Network Traffic Sequences in 5G Core Network Control Plane , year=

Reference 28

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:f95554f185e48cb92223dbb135fdd10d9290499c4f22ec4a694d5f10688aa76b

Observation d73a8af7-c525-492e-ac62-b2b77f231ddc · outbound

This paper cites Anomaly Detection of 5G Control Plane Based on Hidden Semi-Markov Model , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Anomaly Detection of 5G Control Plane Based on Hidden Semi-Markov Model , year=

Reference 29

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:f8fd547216362e0095c0c8e8f151b60c13055928aadf6520163b3ade11dbef33

Observation 00ce7ce0-d319-45a5-a68e-60aeb21d0d3d · outbound

This paper cites UoCAD2: An unsupervised online contextual anomaly detection approach using optimized hyperparameters of RNNs for multivariate time series , journal =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks UoCAD2: An unsupervised online contextual anomaly detection approach using optimized hyperparameters of RNNs for multivariate time series , journal =

Reference 30

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arxiv_id, observed 2026-07-13T01:29:15.984663Z

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=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:ae53c2dcd0af3f14dc3fc92a4a7a34459d9812a55c7891c3495652b2a987b674

Observation 90a7ffed-799b-4803-8c0a-b30252a0c11c · outbound

This paper cites Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition

Reference 31

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:a247af2f3ae07ff483e9a3db4d864e50c75ec7e35d9d248e86c7ac33499e759b

Observation c550f192-46e1-4609-84ef-61ea519365cc · outbound

This paper cites Learning Under Non-stationarity: Covariate Shift Adaptation by Importance Weighting.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Learning Under Non-stationarity: Covariate Shift Adaptation by Importance Weighting

Reference 32

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doi, observed 2026-07-13T01:29:16.018713Z

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

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:be56e73eea5bfbb62eecd9f3993976e7e38937bd8dc73981e7fac71a1dcc14a5

Observation 68d0a165-9733-4fc9-940e-0fbf33ddd7b9 · outbound

This paper cites Proceedings of the 34th International Conference on Machine Learning , pages =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Proceedings of the 34th International Conference on Machine Learning , pages =

Reference 33

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:64af2fdc9c6304f2fe258815b037b4ed08aded0c0692d8057fe03471ad9e4844

Observation 397e952e-af11-453a-bdee-ca4f9f40b1c0 · outbound

This paper cites Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review , year =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review , year =

Reference 34

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doi, observed 2026-07-13T01:29:15.997098Z

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

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:6c3eb3da521582d8e03871c67c18e557635b7cb3291ec7ccb0634b42ed070d76

Observation 76ce4f87-07f5-4a59-9f21-74fe4188495a · outbound

This paper cites and Ibrahim, Noor Farizah and Zainol, Zurinahni and Abdullah, Rosni and Anbar, Mohammed and Alzubaidi, Laith , title =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks and Ibrahim, Noor Farizah and Zainol, Zurinahni and Abdullah, Rosni and Anbar, Mohammed and Alzubaidi, Laith , title =

Reference 35

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:674695349e5c42a257e378f767c76cb52bf11b4e79e13608d128e08c3c052130

Observation d963f2b1-ddb2-4a0c-8397-ba45eade1ec3 · outbound

This paper cites An Experimental Study of Machine Learning-Based Intrusion Detection for OPC UA over Industrial Private 5G Networks , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks An Experimental Study of Machine Learning-Based Intrusion Detection for OPC UA over Industrial Private 5G Networks , year=

Reference 36

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:8e41c8da25e1df70aef0e10c1eec5185bd5a0effa93b236629b1b4e4bafbe305

Observation f7fbad86-1740-45ec-a6d5-9408ac02f525 · outbound

This paper cites OPC UA security analysis , year =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks OPC UA security analysis , year =

Reference 37

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source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:c85dfd0a323950ea98d98cc5f42f98244bf5dd67deb48e8de5bf96ee4d266251

Observation 59e96113-d203-4a72-a8c7-c90a03e154ac · outbound

This paper cites Monitoring encrypted communication with OPC UA , year =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Monitoring encrypted communication with OPC UA , year =

Reference 38

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:c535618d97a9b703103875d5498965d26e70ab7d85206244983c5083c8ca8086

Observation 0046ba57-bb53-4c69-946a-7e3436490193 · outbound

This paper cites Combining Network Data Analytics Function and Machine Learning for Abnormal Traffic Detection in Beyond 5G , year =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Combining Network Data Analytics Function and Machine Learning for Abnormal Traffic Detection in Beyond 5G , year =

Reference 39

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:b5ddcf9986148422c92672187242535a3252833f0bd4a30ced7854836d1f968a

Observation 87ac8f95-79b9-4b6e-be48-14a842fd0392 · outbound

This paper cites Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey , year =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey , year =

Reference 41

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verified exact
arxiv_id, observed 2026-07-13T01:29:16.014005Z

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=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:d11912e280b0fe12c7d3aa23de027e3e684dc1d8b2802c7c6a2089e37f916a1d

Observation d6201df3-66c1-4b1d-a055-9afde69772b1 · outbound

This paper cites Unsupervised Graph-Sequence Anomaly Detection for 5G Core Network Control Plane Traffic , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Unsupervised Graph-Sequence Anomaly Detection for 5G Core Network Control Plane Traffic , year=

Reference 43

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:efe7ec42f3e6b861c0a4e65aec6ac7c6a101490333258869f7305dd0bcd7c5be

Observation 3d1caeef-2f27-4215-b89d-1ca4bdf65233 · outbound

This paper cites 2026 , eprint=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks 2026 , eprint=

Reference 45

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:59ce2948c5ddf19972ac65fd0acb00c1ea9f07588b0dc8f2783aeabbe01f0f66

Observation 51b6b810-8497-4661-b77c-a05964b194ec · outbound

This paper cites Federal Office for Information Security (BSI) , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Federal Office for Information Security (BSI) , year=

Reference 46

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:fe57bfbe3528ba77757b2de1e4141735d2260a3e000a0ba5c739dcf4306bafd9

Observation a360a9fb-068b-4942-ad07-673cdb496555 · outbound

This paper cites International Journal of Service and Knowledge Management , volume=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks International Journal of Service and Knowledge Management , volume=

Reference 47

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:61089752581a2ba3e2d6b63d1646136cd2876595b864df7865c8247b28c42c3a

Observation e2eec363-fd69-45df-99f9-08936de9f3b1 · outbound

This paper cites Combining Network Data Analytics Function and Machine Learning for Abnormal Traffic Detection in Beyond 5G , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Combining Network Data Analytics Function and Machine Learning for Abnormal Traffic Detection in Beyond 5G , year=

Reference 48

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:9ae181ae8bd3afc4b68bcf8b622d29628511d9ec4e226ce7b8eae330009ca972

Observation 54af3184-b403-4e9b-9973-1bd1938021ab · outbound

This paper cites Sensors , VOLUME =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Sensors , VOLUME =

Reference 49

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:50e1dda4a37abea96ed0ffbba66ac2297fbdd0fc98ba65a52f133f630e711a0c

Observation 6f857e78-731e-4b72-bbe2-89fc612e03cb · outbound

This paper cites Information Hiding in Industrial Control Systems: An OPC UA based Supply Chain Attack and its Detection , year =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Information Hiding in Industrial Control Systems: An OPC UA based Supply Chain Attack and its Detection , year =

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-07-13T01:29:16.005961Z

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=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:f2803ef471a3a929115ab5b371462d99ea2a7a3203d71f79449cd291fa675983

Observation 289e6986-5ff6-44cc-9178-2f34da45888a · outbound

This paper cites Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey , year=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey , year=

Reference 51

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:efce9bc186c3156d3fd87ab9eaf551dfb921d652cb0671fc9b50ff9690614575

Observation 4ab49272-8f52-4e49-9509-78dfc9a56021 · outbound

This paper cites Computational Intelligence and Neuroscience , volume =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Computational Intelligence and Neuroscience , volume =

Reference 52

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verified exact
doi, observed 2026-07-13T01:29:15.925105Z

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=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:22aeae52d1d0ff8e42fcbd0b47bbafd76a562103fd4776aa857256e7ef9f3fac

Observation 0653eb9f-8620-49cd-b3db-0973f1d8e0c6 · outbound

This paper cites Computational Intelligence and Neuroscience , volume=.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Computational Intelligence and Neuroscience , volume=

Reference 53

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unresolved
no resolver link, observed 2026-07-13T01:24:25.864311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:ac0f8f8f1a91633325b73cae258f2da09da5fc74e501c61612cd4ce4f4be739e

Observation a919cf72-cc2c-4d2b-95e4-899a719a2b2f · outbound

This paper cites Context-aware anomaly detection by community detection in the Internet of Things , journal =.

Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks Context-aware anomaly detection by community detection in the Internet of Things , journal =

Reference 54

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verified exact
arxiv_id, observed 2026-07-13T01:29:15.933606Z

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=arxiv_source observed=2026-07-13T01:24:25.864311Z digest=sha256:1ff7e36651b9862edb245e1d493070e272b56d2bde15ba2aee8ada8d7b71f4ae

Pith citing papers

No inbound Pith citation observations are available.