Paper Citation Record · LEDGER

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.21703.

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

pith.paper-citation-record.v1
2505.21703 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:28:15.269211Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

35 of 35 outbound references displayed

verified exact3
verified fuzzy27
unresolved4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 138c1101-e361-490f-bfb3-e2fe5a20bc7a · outbound

This paper cites Indus- trial internet of things: Challenges, opportunities, and directions,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Indus- trial internet of things: Challenges, opportunities, and directions,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:19.546409Z

Source-reported events for the cited work

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Observation aed653e5-822f-4d72-b950-6cc6783ad28f · outbound

This paper cites Iot practices in military applications,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Iot practices in military applications,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:19.508312Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 209ecf5a-5f0d-47b9-8e90-75cd852cb0a1 · outbound

This paper cites Security issues in internet of vehicles (iov): A comprehensive survey,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Security issues in internet of vehicles (iov): A comprehensive survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:19.379889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 681ecf15-d602-4416-be5e-20394101552d · outbound

This paper cites An in- depth analysis of the mirai botnet,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks An in- depth analysis of the mirai botnet,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:19.198783Z

Source-reported events for the cited work

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Observation 890e8c65-a135-4d39-b649-c7c9a1b618e9 · outbound

This paper cites The mirai botnet and the iot zombie armies,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks The mirai botnet and the iot zombie armies,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:19.095638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cce5f642-2578-460e-94fd-ace6b0aa9a37 · outbound

This paper cites Mirai ddos attack against kreb- sonsecurity cost device owners $300,000,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Mirai ddos attack against kreb- sonsecurity cost device owners $300,000,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:19.005946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5bf34290-3f58-4280-986c-fae45c8f476d · outbound

This paper cites Predicting machine failures from multivariate time series: An industrial case study,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Predicting machine failures from multivariate time series: An industrial case study,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:18.832021Z

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Observation d7a1ab8e-0f9a-43ea-8d2b-2f34b68ca3cd · outbound

This paper cites Deep learning for time series classification: a review,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Deep learning for time series classification: a review,

Reference 8

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Observation e5e0a78e-6e2a-4917-b01c-29e29c826ab0 · outbound

This paper cites Machine learning-based network vulnerability analysis of industrial internet of things,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Machine learning-based network vulnerability analysis of industrial internet of things,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:18.550047Z

Source-reported events for the cited work

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Observation d647ea08-4df8-47e5-9017-0cd88429e5aa · outbound

This paper cites Online and scalable unsupervised network anomaly detection method,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Online and scalable unsupervised network anomaly detection method,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:18.416096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 72171045-de08-4c7f-95fb-ba395e419c42 · outbound

This paper cites Detection of eavesdrop- ping attack in uav-aided wireless systems: Unsupervised learning with one-class svm and k-means clustering,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Detection of eavesdrop- ping attack in uav-aided wireless systems: Unsupervised learning with one-class svm and k-means clustering,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:18.312201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c345ff47-9362-48a3-a723-a1e1f34bed0b · outbound

This paper cites Deep learning for anomaly detection: Challenges, methods, and opportunities,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Deep learning for anomaly detection: Challenges, methods, and opportunities,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:18.171724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bfd8f089-b072-47ed-82c3-0e64dca99f52 · outbound

This paper cites Anomaly detection for iot time- series data: A survey,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Anomaly detection for iot time- series data: A survey,

Reference 13

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Source-reported events for the cited work

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Observation db705df1-29b5-4852-b1c9-5c9aee3929c4 · outbound

This paper cites Lstm learning with bayesian and gaussian processing for anomaly detection in industrial iot,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Lstm learning with bayesian and gaussian processing for anomaly detection in industrial iot,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:18.036200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6df732dc-fb8c-49aa-b96c-9fa4df9be0c2 · outbound

This paper cites Online anomaly detection with concept drift adaptation using recurrent neural networks,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Online anomaly detection with concept drift adaptation using recurrent neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:17.927732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation daa4bcb8-5d49-4f1f-a949-2258b2093512 · outbound

This paper cites Unsupervised anomaly detection in time series using lstm-based autoencoders,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Unsupervised anomaly detection in time series using lstm-based autoencoders,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:17.807267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 995d4883-60e8-4774-949d-1fe1d13a9fe0 · outbound

This paper cites Anomaly detection methods based on gan: a survey,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Anomaly detection methods based on gan: a survey,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:17.683652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b7c1b75d-fa64-4409-b061-ef83f70dac47 · outbound

This paper cites Dynamic thresholding for video anomaly detection,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Dynamic thresholding for video anomaly detection,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:17.587771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 28d17b4b-70c6-4643-807d-a97d1c083874 · outbound

This paper cites An adversarial contrastive autoencoder for robust multivariate time series anomaly detection,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks An adversarial contrastive autoencoder for robust multivariate time series anomaly detection,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:17.473941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d8ae7883-9764-4f94-8b44-4189805a331a · outbound

This paper cites Contrastive autoencoder for anomaly detection in multivariate time series,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Contrastive autoencoder for anomaly detection in multivariate time series,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:17.305931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d562ac79-9489-4e6c-857c-cb157fdcbb1b · outbound

This paper cites Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:28:15.758623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ae5835e7-70d0-4b55-bcda-b2fd70e940c7 · outbound

This paper cites Structural Attention-Based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Structural Attention-Based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:28:15.613036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d7f2dc2e-c8e4-4464-85e9-ac8250e23732 · outbound

This paper cites Location Anomalies Detection for Connected and Autonomous Vehicles.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Location Anomalies Detection for Connected and Autonomous Vehicles

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:28:15.444788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f34d48f-64a5-48ba-993d-5761f243f7dc · outbound

This paper cites Xai-ads: An explainable artificial intelligence framework for enhancing anomaly detection in autonomous driving systems,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Xai-ads: An explainable artificial intelligence framework for enhancing anomaly detection in autonomous driving systems,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:17.092656Z

Source-reported events for the cited work

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Observation fda0a23d-676d-478d-a60b-167d3f0c3abd · outbound

This paper cites Vanet network traffic anomaly detection using gru- based deep learning model,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Vanet network traffic anomaly detection using gru- based deep learning model,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:16.883470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0ba14ff8-e35d-4f59-b2f9-1bf76de8ee42 · outbound

This paper cites Securing vanets: Multi-objective intrusion detection with variational autoencoders,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Securing vanets: Multi-objective intrusion detection with variational autoencoders,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:16.696933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be352566-116f-499d-88b0-9a1a8276b35b · outbound

This paper cites An Introduction to Autoencoders.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks An Introduction to Autoencoders

Reference 27

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Source-reported events for the cited work

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Observation 733cefb3-14fc-40c2-8659-b3d33158fdf5 · outbound

This paper cites Triplet loss with multistage outlier suppression and class-pair margins for facial expres- sion recognition,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Triplet loss with multistage outlier suppression and class-pair margins for facial expres- sion recognition,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:16.588272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4efb7d90-4d89-4dd5-a13c-5ada1090e1d5 · outbound

This paper cites Two-stage method based on triplet margin loss for pig face recognition,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Two-stage method based on triplet margin loss for pig face recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:16.472517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9bd65fcc-bc6e-46ad-8994-46df56ab5ebf · outbound

This paper cites Facenet: A unified embed- ding for face recognition and clustering,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Facenet: A unified embed- ding for face recognition and clustering,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:16.332987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 75cf4377-12f2-43cf-a5ca-a68cc41b82d5 · outbound

This paper cites Smote: synthetic minority over-sampling technique,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Smote: synthetic minority over-sampling technique,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:16.136450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 313826fe-f875-4710-9337-f4a786a03749 · outbound

This paper cites Pyod: A python toolbox for scalable outlier detection,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Pyod: A python toolbox for scalable outlier detection,

Reference 32

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Source-reported events for the cited work

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Observation 133bf1a6-832c-4e14-9fc6-a2c74b9ee6fd · outbound

This paper cites Deep one-class classification,.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Deep one-class classification,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:16.006133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b7cd5468-0ab8-4c42-8ddb-615dc7f602f4 · outbound

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A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:28:15.878406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6215a0da-382d-411d-9d32-be5a67c638d8 · outbound

This paper cites Available: https://www.zdnet.com/article/ mirai-botnet-attack-against-krebsonsecurity-cost-device-owners-300000/.

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks Available: https://www.zdnet.com/article/ mirai-botnet-attack-against-krebsonsecurity-cost-device-owners-300000/

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:28:18.922935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.