Paper Citation Record · LEDGER

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems

As of 19 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2505.08816.

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

pith.paper-citation-record.v1
2505.08816 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:19:32.834453Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:45:29.648654Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T00:28:23.181563Z

Reference resolution

37 of 37 outbound references displayed

verified exact1
verified fuzzy23
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2f48a79-b0db-4814-afe3-88d01540c7ac · outbound

This paper cites [Online].

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems [Online]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.753986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7e5c935a-d208-4bc2-9f76-1b5b483dd649 · outbound

This paper cites [Online].

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems [Online]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.728262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e358d335-1422-4a48-b902-2494190711b6 · outbound

This paper cites Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:19:33.054756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fac42e4b-789c-4272-8886-d27572590a0b · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems A simple framework for contrastive learning of visual representations,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.607342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 061cbcc4-af37-4aaa-8d36-a894bd9dda17 · outbound

This paper cites SimCSE: Simple Contrastive Learning of Sentence Embeddings.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems SimCSE: Simple Contrastive Learning of Sentence Embeddings

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.613024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 317ab0ef-90d7-4b02-926c-9abf1843f931 · outbound

This paper cites SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.619547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eeb3c964-12fc-425d-80aa-26c72f41c18f · outbound

This paper cites SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.626788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 048d9375-ab5a-4ab9-a525-10f7f3251f9e · outbound

This paper cites Sscl-ids: Enhancing generalization of intrusion detection with self-supervised contrastive learning,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Sscl-ids: Enhancing generalization of intrusion detection with self-supervised contrastive learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.681257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 261c5193-425e-434c-9986-ec0d131e422f · outbound

This paper cites A new hope for network model generalization,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems A new hope for network model generalization,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.659275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f9acecc1-e5b4-40e6-a877-2b62ffffcb88 · outbound

This paper cites Attention is all you need,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Attention is all you need,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.648771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61bdc0ae-ca31-49dc-9736-e275873a2744 · outbound

This paper cites netFound: Principled Design for Network Foundation Models.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems netFound: Principled Design for Network Foundation Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.653813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cec23473-3e9c-4cb2-9d1f-627cb55fc7b0 · outbound

This paper cites Network intrusion detection based on n-gram frequency and time-aware transformer,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Network intrusion detection based on n-gram frequency and time-aware transformer,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.621357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9e52e56e-d580-4428-b459-a22ffc4b354d · outbound

This paper cites RTIDS: A robust transformer-based approach for intrusion detection system,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems RTIDS: A robust transformer-based approach for intrusion detection system,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.596396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2384e59d-3b81-4473-b231-7c5b4df4752f · outbound

This paper cites Flowtransformer: A transformer framework for flow-based network intrusion detection systems,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Flowtransformer: A transformer framework for flow-based network intrusion detection systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.572142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2de511e3-23c6-4083-bcc0-a3ab69e8285b · outbound

This paper cites A Method for Network Intrusion Detection Using Flow Sequence and BERT Framework,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems A Method for Network Intrusion Detection Using Flow Sequence and BERT Framework,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.547216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f568cb75-f481-4f74-a8be-752f77b64cbd · outbound

This paper cites Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.516452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f2d867fb-5934-4ac4-9fc3-7957a883820f · outbound

This paper cites Robust unsupervised network intrusion detection with self-supervised masked context reconstruction,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Robust unsupervised network intrusion detection with self-supervised masked context reconstruction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.492913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a120f98-36a8-4872-b10c-1bcb2b593ea8 · outbound

This paper cites An extreme semi-supervised framework based on transformer for network intrusion detection,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems An extreme semi-supervised framework based on transformer for network intrusion detection,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.466326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b9776538-7639-4654-8f05-b87104e89720 · outbound

This paper cites Et-bert: A contextualized datagram representation with pre-training transformers for encrypted traffic classification,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Et-bert: A contextualized datagram representation with pre-training transformers for encrypted traffic classification,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.711885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f787fe3-0f44-41c5-b7e9-cfeb2d610d36 · outbound

This paper cites NetGPT: Generative Pretrained Transformer for Network Traffic.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems NetGPT: Generative Pretrained Transformer for Network Traffic

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.716684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32f2b2e4-b498-4fcd-976f-ee2233030b5e · outbound

This paper cites Mt-flowformer: A semi-supervised flow transformer for encrypted traffic classification,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Mt-flowformer: A semi-supervised flow transformer for encrypted traffic classification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.423857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 28fff2ce-d338-49a3-b5d1-cedaf75429cd · outbound

This paper cites A neural attention model for real-time network intrusion detection,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems A neural attention model for real-time network intrusion detection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.399796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 087cc40a-4725-4120-a9cc-095c14059a8d · outbound

This paper cites FlowFormers: Transformer-based Models for Real-time Network Flow Classification,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems FlowFormers: Transformer-based Models for Real-time Network Flow Classification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.372565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 637195ac-3fa3-4bd0-9377-334237b8e586 · outbound

This paper cites Real-time Network Intrusion Detection via Decision Transformers.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Real-time Network Intrusion Detection via Decision Transformers

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.746443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae20af2f-c153-4b60-8258-4873e7de8486 · outbound

This paper cites Contrastive learning enhanced intrusion detection,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Contrastive learning enhanced intrusion detection,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.752796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eb47609-caee-4f3c-b900-2dc703a0b74f · outbound

This paper cites Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.322977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation daf169c8-0c73-4008-889e-08a8d6ab67e4 · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Cutmix: Regularization strategy to train strong classifiers with localizable features,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.297620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 909d878c-65ff-4098-9005-3b22d8e4fcd3 · outbound

This paper cites Anomal-E: A self-supervised network intrusion detection system based on graph neural networks,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Anomal-E: A self-supervised network intrusion detection system based on graph neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.268869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6da2b41f-25b2-4d1e-8d70-3d59824ee64f · outbound

This paper cites Toward generating a new intrusion detection dataset and intrusion traffic characterization.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Toward generating a new intrusion detection dataset and intrusion traffic characterization

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.243987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fe98d1da-42e3-4905-9854-5f6eee8bf33c · outbound

This paper cites UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.783986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 830bbca8-5377-4bfa-a7f9-f1195f40a9dc · outbound

This paper cites Troubleshooting an intrusion detection dataset: the CICIDS2017 case study,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Troubleshooting an intrusion detection dataset: the CICIDS2017 case study,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.202423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1b57ff70-d834-424f-90ed-f020898718b4 · outbound

This paper cites New directions in automated traffic analysis,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems New directions in automated traffic analysis,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.174138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3dc401df-7b0b-4bb4-8fd9-6dd91b3b4ead · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.807410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c335c463-c85c-4ddb-bbfd-36a044f257f7 · outbound

This paper cites Improving language understanding by generative pre-training,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Improving language understanding by generative pre-training,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T22:19:32.813694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b51482fb-263a-43c0-ac88-715f4b77b733 · outbound

This paper cites Error Prevalence in NIDS datasets: A Case Study on CIC-IDS-2017 and CSE-CIC-IDS-2018,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Error Prevalence in NIDS datasets: A Case Study on CIC-IDS-2017 and CSE-CIC-IDS-2018,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.126957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2a24f3ed-3410-4cc7-bd49-e3c0efb02649 · outbound

This paper cites An empirical comparison of botnet detection methods,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems An empirical comparison of botnet detection methods,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.104020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5490e4c0-7932-42f2-9ec0-ef0ea77f3a91 · outbound

This paper cites Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy,.

Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:19:33.080101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation ad5e9a27-7ac0-4b7f-959a-ffb3ca26becc · inbound

A Quantum Genetic Algorithm-Enhanced Self-Supervised Intrusion Detection System for Wireless Sensor Networks in the Internet of Things cites this paper.

A Quantum Genetic Algorithm-Enhanced Self-Supervised Intrusion Detection System for Wireless Sensor Networks in the Internet of Things Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T10:45:29.648654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed462e24-d04b-41b1-abb2-b0cb03ac8623 · inbound

CSTS: A Canonical Security Telemetry Substrate for AI-Native Cyber Detection cites this paper.

CSTS: A Canonical Security Telemetry Substrate for AI-Native Cyber Detection Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:28:23.183630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.