Paper Citation Record · LEDGER

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs

As of 17 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2509.06550.

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

pith.paper-citation-record.v1
2509.06550 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:37:45.941122Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

40 of 40 outbound references displayed

verified exact4
verified fuzzy23
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 730ec18e-a55b-402f-8cb6-3aa6dcc30b19 · outbound

This paper cites A taxonomy of network threats and the effect of current datasets on intrusion detection systems,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs A taxonomy of network threats and the effect of current datasets on intrusion detection systems,

Reference 1

Resolution: metadata mismatch; raw_fallback, observed 2026-08-04T23:37:46.837248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 542eaf6f-d5b5-436e-86a7-fb41829f5398 · outbound

This paper cites Explainable cross-domain evaluation of ml-based network intrusion detection systems,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Explainable cross-domain evaluation of ml-based network intrusion detection systems,

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-04T23:37:46.675860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4cef9d2f-f834-4a47-83e5-3aa0b0bf308c · outbound

This paper cites Towards an effective zero-day attack detection using outlier-based deep learning techniques,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Towards an effective zero-day attack detection using outlier-based deep learning techniques,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:50.334786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 68228410-16cb-4721-af92-bb15a5d59f1c · outbound

This paper cites Anomaly detection using replicator neural networks trained on examples of one class,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Anomaly detection using replicator neural networks trained on examples of one class,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:50.198843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 16c4dd59-5cb5-437e-af71-93ea063beabf · outbound

This paper cites A cookbook of self-supervised learning,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs A cookbook of self-supervised learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:50.084651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a298224a-ba7b-4453-8757-c225115ac14f · outbound

This paper cites Conflow: Contrast network flow improving class-imbalanced learning in network intrusion detection,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Conflow: Contrast network flow improving class-imbalanced learning in network intrusion detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:50.011060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1cece8ab-14d1-4e4f-b5e2-a430475bd5e7 · outbound

This paper cites Contrastive learning enhanced intrusion detection,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Contrastive learning enhanced intrusion detection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:49.914502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 070391e2-23dc-4829-b1c6-6a4af83c690c · outbound

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

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Sscl-ids: Enhancing generalization of intrusion detection with self-supervised contrastive learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:49.817719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 50bf3db5-4861-46c4-a99a-1bb454373046 · outbound

This paper cites Network intrusion detection model based on improved byol self-supervised learning,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Network intrusion detection model based on improved byol self-supervised learning,

Reference 9

Resolution: verified exact; doi, observed 2026-08-04T23:37:46.060025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4097d10f-427d-4f1a-94c7-fb1854148941 · outbound

This paper cites An investigation into the performance of non-contrastive self-supervised learning methods for network intrusion detection,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs An investigation into the performance of non-contrastive self-supervised learning methods for network intrusion detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:49.610327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6779b143-6e50-436b-85c2-3736253af9a5 · outbound

This paper cites An intrusion detection model based on feature reduction and convolutional neural networks,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs An intrusion detection model based on feature reduction and convolutional neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:49.413733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 615f8c3b-3d2d-4789-a1e2-de51a587f8ad · outbound

This paper cites A cnn-lstm model for intrusion detection system from high dimensional data,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs A cnn-lstm model for intrusion detection system from high dimensional data,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:49.254895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4fe68c6e-9527-4f3f-93f0-02afb4b7cb15 · outbound

This paper cites Hassen and P.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Hassen and P

Reference 13

Resolution: verified exact; doi, observed 2026-08-04T23:37:45.996999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 028fa139-e8dd-461e-bcf7-d10b3ef88434 · outbound

This paper cites A grassmannian approach to zero-shot learning for network intrusion detection,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs A grassmannian approach to zero-shot learning for network intrusion detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:49.102480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2f3495c3-9732-4a74-8901-cf7382dbad99 · outbound

This paper cites Anomaly based unknown intrusion detection in endpoint environments,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Anomaly based unknown intrusion detection in endpoint environments,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:48.883012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5683283f-252b-4a08-a70d-6f19b892dba2 · outbound

This paper cites Network intrusion detector based on isolation . . . forest algorithm,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Network intrusion detector based on isolation . . . forest algorithm,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:48.698145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0d07a346-4d28-4190-b8ae-7f1cd26d5157 · outbound

This paper cites Unknown attack detection based on zero-shot learning,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Unknown attack detection based on zero-shot learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:48.448406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 80527c76-4db8-4502-88e1-9c4a95786432 · outbound

This paper cites Deep unsupervised anomaly detec- tion,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Deep unsupervised anomaly detec- tion,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:48.300453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2840ecaa-8b37-42a4-96ad-e3c29e909510 · outbound

This paper cites Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-04T23:37:46.447825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 52406019-099a-4cf0-b0ce-945503c1554b · outbound

This paper cites Deep learning approach combining sparse autoencoder with svm for network intrusion detection,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Deep learning approach combining sparse autoencoder with svm for network intrusion detection,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:48.202843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5a455764-b290-48e6-bcec-e759a36fd059 · outbound

This paper cites Deep one-class classification,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Deep one-class classification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:48.000128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59fffd85-e29b-455d-aa2a-9e2220ccfcd9 · outbound

This paper cites Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-04T23:37:46.386784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3a1258fd-270c-462e-bc64-5113357075f5 · outbound

This paper cites Efficient malware originated traffic classification by using generative adversarial networks,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Efficient malware originated traffic classification by using generative adversarial networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:47.789836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4fcf278e-1e77-4762-8f25-6bcf1b852870 · outbound

This paper cites Network intrusion detection based on supervised adversarial variational auto-encoder with regularization,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Network intrusion detection based on supervised adversarial variational auto-encoder with regularization,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:47.570452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e8016f9b-7254-4ab9-aa01-d3a5d63be0c1 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs LLaMA: Open and Efficient Foundation Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:45.433741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 148dad4d-1fe0-43ec-91b2-8a5db5916652 · outbound

This paper cites Colorful Image Colorization.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Colorful Image Colorization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:45.466326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc0da397-fb40-4859-9a90-0c6431f51d7b · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs A Simple Framework for Contrastive Learning of Visual Representations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:45.506862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6053dd48-0516-42a0-8a7c-891bb3f07984 · outbound

This paper cites InfoNCE: Identifying the Gap Between Theory and Practice.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs InfoNCE: Identifying the Gap Between Theory and Practice

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:45.539182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4115727e-a40f-4972-bf50-16c77a888ab5 · outbound

This paper cites Bootstrap your own latent: A new approach to self-supervised Learning.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Bootstrap your own latent: A new approach to self-supervised Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:45.572852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf87bd3f-30a5-460c-9628-bb226908c172 · outbound

This paper cites Exploring simple siamese representation learning,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Exploring simple siamese representation learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:47.440661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a3b5893c-6c8d-4bf3-b0f4-26d41ce19249 · outbound

This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:45.675695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18db0081-64e5-419b-8162-93bbe351f7be · outbound

This paper cites Barlow Twins: Self-Supervised Learning via Redundancy Reduction.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:45.700601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d128140-1645-40a0-b56e-ee12aa88ace6 · outbound

This paper cites Dimensionality reduction by learning an invariant mapping,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Dimensionality reduction by learning an invariant mapping,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:47.347827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9255c43d-9e3b-41e9-85a9-e69255637cf2 · outbound

This paper cites Learning a similarity metric discriminatively, with application to face verification,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Learning a similarity metric discriminatively, with application to face verification,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:47.244318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fface375-3f02-4653-9940-fa39d3755aa3 · outbound

This paper cites Improved deep metric learning with multi-class n-pair loss objective,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Improved deep metric learning with multi-class n-pair loss objective,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:47.056405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 46033cef-4633-4b9e-92b8-8d886113f907 · outbound

This paper cites From cic-ids2017 to lycos-ids2017: A corrected dataset for better performance,.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs From cic-ids2017 to lycos-ids2017: A corrected dataset for better performance,

Reference 37

Resolution: metadata mismatch; raw_fallback, observed 2026-08-04T23:37:46.225466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c2c513d5-0112-4af4-99ca-29ead3ef6ce4 · outbound

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

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Toward generating a new intrusion detection dataset and intrusion traffic characterization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:37:46.967589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 97214393-547d-4d70-ae63-76d7d028f0d5 · outbound

This paper cites Exploring Simple Siamese Representation Learning.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Exploring Simple Siamese Representation Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:45.641226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 452ae844-b176-47ae-baf7-4cc708f896b6 · outbound

This paper cites Understanding self-supervised Learning Dynamics without Contrastive Pairs.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs Understanding self-supervised Learning Dynamics without Contrastive Pairs

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:45.763788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41b99fb0-2723-4182-955c-4eda6f7c4eee · outbound

This paper cites A Cookbook of Self-Supervised Learning.

Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs A Cookbook of Self-Supervised Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T23:37:44.476309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.