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Paper Citation Record · LEDGER

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection

As of 21 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2607.18479.

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

pith.paper-citation-record.v1
2607.18479 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:21:25.747539Z

measured 56 of 56 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

56 of 56 outbound references displayed

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  • verified fuzzy0
  • unresolved42
  • parse uncertain0
  • malformed identifier6
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e2c57c43-8252-4141-a8ec-aff79be02531 · outbound

This paper cites A step-by- step training method for multi generator gans with application to anomaly detection and cybersecurity.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection A step-by- step training method for multi generator gans with application to anomaly detection and cybersecurity

Reference 1

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Observation 03425668-c00a-4967-8f1e-ad8dc708c150 · outbound

This paper cites an unresolved cited work.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Unresolved cited work

Reference 2

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Observation c19fe85b-29e2-4a9d-93cd-dc81ba90a09b · outbound

This paper cites an unresolved cited work.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Unresolved cited work

Reference 3

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Observation 6ebc2e86-ca8c-4d45-a0c6-4e2f20cf75a2 · outbound

This paper cites Computer security threat monitoring and surveil- lance.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Computer security threat monitoring and surveil- lance

Reference 4

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Observation 8d15a5b8-dd62-40ee-baf2-157491233596 · outbound

This paper cites A review on application of gans in cybersecurity domain.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection A review on application of gans in cybersecurity domain

Reference 5

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Observation 78cd13bf-bd6e-4edc-9bc8-e9ae0acb66a5 · outbound

This paper cites A Benchmark of Medical Out of Distribution Detection.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection A Benchmark of Medical Out of Distribution Detection

Reference 6

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Observation 35b2fdd5-7fb1-4d43-90c2-7d3c56d6d202 · outbound

This paper cites Deep Learning for Anomaly Detection: A Survey.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Deep Learning for Anomaly Detection: A Survey

Reference 7

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Observation 8e6f1b86-e1e3-4f84-8132-7fa58433bf01 · outbound

This paper cites Deep generative model with hierarchical latent factors for time series anomaly detection, in: Camps-Valls, G., Ruiz, F.J.R., Valera, I.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Deep generative model with hierarchical latent factors for time series anomaly detection, in: Camps-Valls, G., Ruiz, F.J.R., Valera, I

Reference 8

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Observation 2210cec0-d0cd-4550-95b5-c38b7a2d33d9 · outbound

This paper cites Anomaly detection: A survey.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Anomaly detection: A survey

Reference 9

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Observation 7ab22681-7085-4f27-8666-f35317e6ca23 · outbound

This paper cites Semi-supervised anomaly detection via reinforcement learning-enabled method with causal inference.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Semi-supervised anomaly detection via reinforcement learning-enabled method with causal inference

Reference 10

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Observation a17b8947-28d2-4aad-a74b-e0f96bbe59ae · outbound

This paper cites On tensors, sparsity, and nonnega- tive factorizations.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection On tensors, sparsity, and nonnega- tive factorizations

Reference 11

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Observation dbaab17b-1085-4e78-ad89-1c1adb547402 · outbound

This paper cites Classification of red team authentication events in an enterprise network, in: Machine Learning and Knowledge Dis- covery for Cybersecurity.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Classification of red team authentication events in an enterprise network, in: Machine Learning and Knowledge Dis- covery for Cybersecurity

Reference 12

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Observation 792a30d9-f12c-4d53-9413-ca20ceab1090 · outbound

This paper cites An intrusion-detection model.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection An intrusion-detection model

Reference 13

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Observation 32940459-f1e1-41d7-b33a-6f19d07e42b3 · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection NICE: Non-linear Independent Components Estimation

Reference 14

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Observation f7b4dad5-ac29-4716-9db3-5280a161ff1e · outbound

This paper cites Density estimation using Real NVP.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Density estimation using Real NVP

Reference 15

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Observation 91b499ea-2ff3-4d57-a194-e8acb7452ef4 · outbound

This paper cites A compre- hensive survey of generative adversarial networks (gans) in cybersecu- rity intrusion detection.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection A compre- hensive survey of generative adversarial networks (gans) in cybersecu- rity intrusion detection

Reference 16

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Observation 421cd2a1-697f-41e3-b611-69190ff27d4c · outbound

This paper cites Variationalautoencodersusingconvolutional neural network for highly advanced cyber threats, in: 2024 IEEE Inte- grated STEM Education Conference (ISEC), pp.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Variationalautoencodersusingconvolutional neural network for highly advanced cyber threats, in: 2024 IEEE Inte- grated STEM Education Conference (ISEC), pp

Reference 17

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Observation d261647b-7f23-4fdc-b371-5dbf02008666 · outbound

This paper cites Multi-dimensional anomalous entity detection via poisson tensor factorization, in: 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), pp.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Multi-dimensional anomalous entity detection via poisson tensor factorization, in: 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), pp

Reference 18

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Observation c85e2733-e96d-48bb-8831-fba32b557105 · outbound

This paper cites pycp_apr.https://github.com/lanl/pyCP_ APR.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection pycp_apr.https://github.com/lanl/pyCP_ APR

Reference 19

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Observation 0d3a216a-f452-49d1-b67d-6298b37d6a53 · outbound

This paper cites General-purpose unsupervised cy- ber anomaly detection via non-negative tensor factorization.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection General-purpose unsupervised cy- ber anomaly detection via non-negative tensor factorization

Reference 20

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Observation 656630d6-c606-4269-a4b3-e7792be5aa3a · outbound

This paper cites Using collab- orative filtering to weave an information tapestry.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Using collab- orative filtering to weave an information tapestry

Reference 21

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Observation 5047acfe-cb0f-4167-a02c-523586d709ec · outbound

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Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Unresolved cited work

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Observation 1d8ea9e9-b4a2-4fc0-8431-69dcb1ad1d95 · outbound

This paper cites A normalizing flow- based semi-supervised method for imbalanced network intrusion detec- tion.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection A normalizing flow- based semi-supervised method for imbalanced network intrusion detec- tion

Reference 23

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Observation 41c9ed69-3329-447e-a7b8-71fd8f696669 · outbound

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Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Unresolved cited work

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Observation 8054d1ff-a22e-4db0-81d5-4baa4e4b7471 · outbound

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Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Unresolved cited work

Reference 25

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Observation 32d916b6-d1d5-4220-93f9-baf192fdf8df · outbound

This paper cites Why normal- izing flows fail to detect out-of-distribution data, in: Advances in Neural Information Processing Systems (NeurIPS), pp.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Why normal- izing flows fail to detect out-of-distribution data, in: Advances in Neural Information Processing Systems (NeurIPS), pp

Reference 26

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Observation 7ec76465-18c9-4f25-9811-298566ebf146 · outbound

This paper cites Normalizing flows: An introduction and review of current methods.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Normalizing flows: An introduction and review of current methods

Reference 27

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Observation 436ad481-e1c7-4e13-93c8-6ba1a9651f3b · outbound

This paper cites Msattnflow: Normalizing flow for unsuper- vised anomaly detection with multi-scale attention.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Msattnflow: Normalizing flow for unsuper- vised anomaly detection with multi-scale attention

Reference 28

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Observation 39e8383b-4709-45dc-a6dc-5ffbabdedc88 · outbound

This paper cites The Program with a Personality: Analysis of Elk Cloner, the First Personal Computer Virus.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection The Program with a Personality: Analysis of Elk Cloner, the First Personal Computer Virus

Reference 29

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Observation cf5abc08-9aa2-4654-8c0f-7b4c223bfbaf · outbound

This paper cites Hyperband: A novel bandit-based approach to hyperparameter optimization.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Hyperband: A novel bandit-based approach to hyperparameter optimization

Reference 30

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Observation 212adbe5-e2be-4900-96ec-48af2eef20f2 · outbound

This paper cites Anomaly detection in large-scale networks with latent space mod- els.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Anomaly detection in large-scale networks with latent space mod- els

Reference 31

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Observation 74557edd-6f8e-40af-ad9a-11926478b1d9 · outbound

This paper cites Do deep generative models know what they don’t know?, in: International Conference on Learning Representations (ICLR).

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Do deep generative models know what they don’t know?, in: International Conference on Learning Representations (ICLR)

Reference 32

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Observation 7636cd96-9efa-49c0-b02a-a1095d3dd08f · outbound

This paper cites Phishnet- vae cybersecurity approach: An integrated variational autoencoder and deep neural network approach for enhancing cybersecurity strategies by detecting phishing attacks.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Phishnet- vae cybersecurity approach: An integrated variational autoencoder and deep neural network approach for enhancing cybersecurity strategies by detecting phishing attacks

Reference 33

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Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Unresolved cited work

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Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Normalizing flows for probabilistic modeling and inference

Reference 35

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Observation 603478ca-d659-4f50-99e2-f986af8c2479 · outbound

This paper cites Graph link prediction in computer networks using Poisson matrix factorisation.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Graph link prediction in computer networks using Poisson matrix factorisation

Reference 36

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Observation 7bda2036-2772-4bb9-a2c9-95f480f180ad · outbound

This paper cites Understanding likelihood of normalizing flow and image complexity through the lens of out-of- distribution detection.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Understanding likelihood of normalizing flow and image complexity through the lens of out-of- distribution detection

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Observation 89a68fb5-fee9-41cb-9f3a-6d2d821755db · outbound

This paper cites Likelihood ratios for out-of-distribution de- tection, in: Advances in Neural Information Processing Sys- tems (NeurIPS).

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Likelihood ratios for out-of-distribution de- tection, in: Advances in Neural Information Processing Sys- tems (NeurIPS)

Reference 38

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Observation 0b4a8e2c-1c2c-440a-bd89-e7e1dffdf453 · outbound

This paper cites Same same but differnet: Semi-supervised defect detection with normalizing flows, in: Proceed- ings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Same same but differnet: Semi-supervised defect detection with normalizing flows, in: Proceed- ings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp

Reference 39

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Observation 9ea67bd8-88d9-482b-aea5-e2cff8b554d3 · outbound

This paper cites Time of day anomaly detection, in: 2018 European Intelligence and Security Infor- matics Conference (EISIC), IEEE.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Time of day anomaly detection, in: 2018 European Intelligence and Security Infor- matics Conference (EISIC), IEEE

Reference 40

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Observation df3f9d20-82f0-48bc-942c-1b6ed4d695c3 · outbound

This paper cites Input complexity and out-of-distribution detection with likelihood-based generative models, in: International Conference on Learning Representations (ICLR).

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Input complexity and out-of-distribution detection with likelihood-based generative models, in: International Conference on Learning Representations (ICLR)

Reference 41

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Observation 54a18527-6de3-4438-a630-05f684b579aa · outbound

This paper cites Generative Adversarial Networks (GAN) In- sights for Cyber Security Applications.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Generative Adversarial Networks (GAN) In- sights for Cyber Security Applications

Reference 42

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source=pdf_text observed=2026-08-01T15:21:24.829975Z digest=sha256:8c4312992df7060de19bdffa8ef2f4023c730c850bb85e75b5ed9bec62892655

Observation fd05dc22-0726-42b5-9df1-752a73079035 · outbound

This paper cites Variational Autoencoder (VAE) for Anomaly De- tection in Network Traffic.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Variational Autoencoder (VAE) for Anomaly De- tection in Network Traffic

Reference 43

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Observation 05c601f1-3d7e-427e-93ff-097ee0d479f2 · outbound

This paper cites Using variational autoen- coders with machine learning algorithms in cyber security applications.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Using variational autoen- coders with machine learning algorithms in cyber security applications

Reference 44

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Observation 848cbec9-8b7e-4570-8899-8f86d68dc901 · outbound

This paper cites Poisson fac- torization for peer-based anomaly detection, in: 2016 IEEE Confer- ence on Intelligence and Security Informatics (ISI), IEEE.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Poisson fac- torization for peer-based anomaly detection, in: 2016 IEEE Confer- ence on Intelligence and Security Informatics (ISI), IEEE

Reference 45

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Observation 0aad0dbb-5750-419e-afe3-ebcff3238526 · outbound

This paper cites Prediction of industrial cyber attacks using normalizing flows.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Prediction of industrial cyber attacks using normalizing flows

Reference 46

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Observation 1b84523a-a4c7-41e3-83e6-b834f600aa49 · outbound

This paper cites Maximizing anomalydetectionperformanceusinglatentvariablemodelsinindustrial systems.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Maximizing anomalydetectionperformanceusinglatentvariablemodelsinindustrial systems

Reference 47

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Observation 446e88b6-ee5d-4937-91a3-8996e29ec586 · outbound

This paper cites Application of uncertainty to out-of-distribution detection for autonomous driving perception safety.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Application of uncertainty to out-of-distribution detection for autonomous driving perception safety

Reference 48

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Observation 673860e3-fd5a-4390-abc5-9c0b9a0eec5e · outbound

This paper cites Unified Host and Network Data Set.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Unified Host and Network Data Set

Reference 49

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Observation 8af3b0e7-bb78-42c8-b28a-525e15b65b50 · outbound

This paper cites Understanding fail- ures in out-of-distribution detection with deep generative models, in: 32 Proceedings of the 38th International Conference on Machine Learning (ICML), pp.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Understanding fail- ures in out-of-distribution detection with deep generative models, in: 32 Proceedings of the 38th International Conference on Machine Learning (ICML), pp

Reference 50

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Observation 003f6896-b99a-4dc9-897a-b8ef6ce85595 · outbound

This paper cites Improving out-of-distribution detection in normalizing flows with synthetic outliers.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Improving out-of-distribution detection in normalizing flows with synthetic outliers

Reference 51

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Observation 11d9b545-3e91-437a-98a5-4a9dcf375a84 · outbound

This paper cites Generalized out-of-distribution detection: A survey.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Generalized out-of-distribution detection: A survey

Reference 52

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Observation 1cb360ae-060b-4865-8f17-967e37dcac95 · outbound

This paper cites Msflow: Multi- scale flow-based framework for unsupervised anomaly detection.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Msflow: Multi- scale flow-based framework for unsupervised anomaly detection

Reference 53

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Observation 9953de29-a2f0-4304-95cf-e5e0547fddc7 · outbound

This paper cites Semi- supervised anomaly detection via neural process.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Semi- supervised anomaly detection via neural process

Reference 55

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Observation 12dca1e7-ef3c-4a23-b9fc-c53528b35b87 · outbound

This paper cites an unresolved cited work.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Unresolved cited work

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Observation 4da319ea-372e-4288-8ff3-7e7d6fbe775b · outbound

This paper cites Out-of-distribution Detection in Medical Image Analysis: A survey.

Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection Out-of-distribution Detection in Medical Image Analysis: A survey

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