Paper Citation Record · LEDGER

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks

As of 23 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2507.20373.

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

pith.paper-citation-record.v1
2507.20373 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:38:53.246573Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

27 of 27 outbound references displayed

verified exact1
verified fuzzy21
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f373eb12-e7e5-4088-88d2-3c25d2bf7ea5 · outbound

This paper cites Emerging trends in uavs: From placement, semantic communications to generative ai for mission-critical networks,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Emerging trends in uavs: From placement, semantic communications to generative ai for mission-critical networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:58.888159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 17690c41-ece1-4b14-8d6b-03414a5f006f · outbound

This paper cites X-cba: Ex- plainability aided catboosted anomal-e for intrusion detection system,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks X-cba: Ex- plainability aided catboosted anomal-e for intrusion detection system,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:58.728634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 347a7447-a8d8-4372-849f-573a1cacf18a · outbound

This paper cites Investigating on black holes in segment routing networks: Identification and detection,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Investigating on black holes in segment routing networks: Identification and detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:58.586475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0dfcb4ec-da99-4a2a-9199-892e3e5be55c · outbound

This paper cites Black hole prediction in backbone networks: A com- prehensive and type-independent forecasting model,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Black hole prediction in backbone networks: A com- prehensive and type-independent forecasting model,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:58.335163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 040ed515-8e6e-4e37-9a08-0f62bd04d454 · outbound

This paper cites Applications of generative adversarial networks in anomaly detection: A systematic literature review,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Applications of generative adversarial networks in anomaly detection: A systematic literature review,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:57.931746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fcdcb3c3-8029-4a83-ac34-a01da06a3e88 · outbound

This paper cites Bgp anomaly detection techniques: A survey,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Bgp anomaly detection techniques: A survey,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:57.587673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d5f025a3-d953-41e1-97b6-cd229405d15e · outbound

This paper cites Deep learning for network intrusion: A hierarchical approach to reduce false alarms,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Deep learning for network intrusion: A hierarchical approach to reduce false alarms,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:57.390651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 76980793-8ec3-4940-b006-87a0bad803da · outbound

This paper cites Toward developing efficient conv-ae-based intrusion detection system using heterogeneous dataset,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Toward developing efficient conv-ae-based intrusion detection system using heterogeneous dataset,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:57.227087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 36eb037a-12d8-4174-996a-4028bbd828ce · outbound

This paper cites Knacks of a hybrid anomaly detection model using deep auto-encoder driven gated recurrent unit,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Knacks of a hybrid anomaly detection model using deep auto-encoder driven gated recurrent unit,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:57.009063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3bfe4a24-01bc-4e58-99ff-0fea43f14787 · outbound

This paper cites Cannolo: An anomaly detection system based on lstm autoencoders for controller area network,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Cannolo: An anomaly detection system based on lstm autoencoders for controller area network,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:56.785822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 48f11fec-eded-4db3-bbea-6f58b2829111 · outbound

This paper cites Dct-gan: Dilated convolutional transformer-based gan for time series anomaly detection,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Dct-gan: Dilated convolutional transformer-based gan for time series anomaly detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:56.456542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 02e9f225-31dc-452c-8943-f87eadec642a · outbound

This paper cites Transec-gan: A transformer-enhanced ids for robust detection and privacy in industrial cps,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Transec-gan: A transformer-enhanced ids for robust detection and privacy in industrial cps,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:56.310875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 87e378ff-c322-4720-9944-8097cf6c720f · outbound

This paper cites Mul- tivariate time series anomaly detection with adversarial transformer architecture in the internet of things,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Mul- tivariate time series anomaly detection with adversarial transformer architecture in the internet of things,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:56.058442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fa014950-2295-4e08-803c-d9c5d22c793c · outbound

This paper cites Transformer or autoencoder? who is the ultimate adversary for attack detectors?.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Transformer or autoencoder? who is the ultimate adversary for attack detectors?

Reference 14

Resolution: verified exact; doi, observed 2026-08-06T13:38:53.552762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 72b2b18b-b13d-4580-86e0-c09a1ae8ec24 · outbound

This paper cites Transformer-based gan-augmented defender for adversarial usb keystroke injection attacks,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Transformer-based gan-augmented defender for adversarial usb keystroke injection attacks,

Reference 15

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T13:38:53.857483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c922f9b0-7039-4c43-bfa1-b4b8852ffcd6 · outbound

This paper cites Next-gen metaverse security through intrusion detection enhanced by transformers and gans,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Next-gen metaverse security through intrusion detection enhanced by transformers and gans,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:55.836334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fae02c96-c9c9-40fe-bb65-0a2265f914b3 · outbound

This paper cites Feature selection for black hole attacks.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Feature selection for black hole attacks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:55.690999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ff8b5778-930e-4cb0-8ef3-b94d7707474f · outbound

This paper cites Blackhole attack detection using machine learning approach on manet,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Blackhole attack detection using machine learning approach on manet,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:55.486392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8b2b2cc9-3b03-4740-9410-d10d875c4b5b · outbound

This paper cites Machine learning models to detect the blackhole attack in wireless adhoc network,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Machine learning models to detect the blackhole attack in wireless adhoc network,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:55.202759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4e3f56f7-ebe3-4c43-9de7-de870172cf1d · outbound

This paper cites Wsn-ds: A dataset for intrusion detection systems in wireless sensor networks,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Wsn-ds: A dataset for intrusion detection systems in wireless sensor networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:54.853333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c165cf6e-3d9d-4bbb-bf36-ee75a3578c8d · outbound

This paper cites Wasserstein generative ad- versarial networks,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Wasserstein generative ad- versarial networks,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T13:38:52.309721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bceb5275-8f26-4a2f-aa71-a1dddf917167 · outbound

This paper cites Einops: Clear and reliable tensor manipulations with einstein-like notation,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Einops: Clear and reliable tensor manipulations with einstein-like notation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:54.577744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 51073093-38f8-47b8-930d-e06d8456b153 · outbound

This paper cites Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:54.385591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1d307196-84ad-41ca-8e55-71b6a77c9698 · outbound

This paper cites Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T13:38:52.764530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bad872d8-683a-4d1d-9a68-ae672dd08359 · outbound

This paper cites f-anogan: Fast unsupervised anomaly detection with generative adversarial networks,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks f-anogan: Fast unsupervised anomaly detection with generative adversarial networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:54.171180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5df7281a-192c-43a8-85b6-8363c1392e71 · outbound

This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T13:38:53.085717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92161770-91ea-41de-a85a-d8a3f8fb7250 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting,.

WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T13:38:53.246573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.