Paper Citation Record · LEDGER

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017

As of 19 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2506.19877.

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

pith.paper-citation-record.v1
2506.19877 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:19:27.827066Z

measured 28 of 28 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:19:07.018781Z

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 exact7
verified fuzzy14
unresolved5
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bdd36036-5533-4030-b9e2-ee73e0f2314d · outbound

This paper cites Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:19:29.745747Z

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 cb4b7400-27f5-43d6-bbd6-2a7abc639179 · outbound

This paper cites The evaluation of network anomaly detection systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 The evaluation of network anomaly detection systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:19:25.104211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3bff470-b00e-4fc9-a2fd-7700c9e60f8b · outbound

This paper cites Outside the closed world: On using machine learning for network intrusion detection,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Outside the closed world: On using machine learning for network intrusion detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:32.888642Z

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 9c9299f8-80f4-47da-9169-2023a8fd430c · outbound

This paper cites Anomaly detection: A survey,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Anomaly detection: A survey,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:19:25.344733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d1462c1-aa47-4cbe-a937-3c66edba925c · outbound

This paper cites Evaluation of CI- CIDS2017 with qualitative comparison of machine learning algorithm,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Evaluation of CI- CIDS2017 with qualitative comparison of machine learning algorithm,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:32.627428Z

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 a3cbbddc-7034-4693-9013-fd6a138469cc · outbound

This paper cites Research on enhancing cloud computing network security using artificial intelligence algorithms,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Research on enhancing cloud computing network security using artificial intelligence algorithms,

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-06T23:19:29.292659Z

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 a7bf823f-37b2-4646-bc4e-15aef029d86b · outbound

This paper cites A hybrid deep learning anomaly detection framework for intrusion detection,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 A hybrid deep learning anomaly detection framework for intrusion detection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:32.451645Z

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 63e3c8e2-60ed-49f9-9fe2-8be41f8e9edd · outbound

This paper cites Anomaly based intrusion detection system: A deep learning approach,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Anomaly based intrusion detection system: A deep learning approach,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:32.226947Z

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 ab4b7b94-f8cf-4da0-8808-84d7e1f3f3ad · outbound

This paper cites A machine learning approach for intrusion detection system on NSL-KDD dataset,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 A machine learning approach for intrusion detection system on NSL-KDD dataset,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:32.001045Z

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 232f7848-eb55-40c9-a65f-12c26803d322 · outbound

This paper cites A detailed analysis of the KDD Cup 99 data set,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 A detailed analysis of the KDD Cup 99 data set,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:31.766430Z

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 7914c36e-c78a-4092-bbc4-d3a1db11f378 · outbound

This paper cites A new deep learning based intrusion detection system for cloud security,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 A new deep learning based intrusion detection system for cloud security,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:31.535538Z

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 b5bb0e9d-58dc-43cd-985b-ec7993eda37e · outbound

This paper cites Evaluating supervised learning models for fraud detection: A comparative study of classical and deep architectures on imbalanced transaction data,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Evaluating supervised learning models for fraud detection: A comparative study of classical and deep architectures on imbalanced transaction data,

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-06T23:19:29.045799Z

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 bd0c001d-17e0-4607-ab7d-c9aea086044c · outbound

This paper cites Estimating the support of a high-dimensional distribution,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Estimating the support of a high-dimensional distribution,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:31.345264Z

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 dacb3377-e612-4507-9e32-422374d856a3 · outbound

This paper cites UltraRE: Enhancing RecEraser for recommendation unlearning via error decomposition,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 UltraRE: Enhancing RecEraser for recommendation unlearning via error decomposition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:31.095756Z

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 eb186f5f-e8ab-497a-8fd9-d86a54c826b0 · outbound

This paper cites SETransformer: A hy- brid attention-based architecture for robust human activity recognition,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 SETransformer: A hy- brid attention-based architecture for robust human activity recognition,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:30.822044Z

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 72104a8a-0e3a-44bc-a1d2-8951a66bdd46 · outbound

This paper cites Pruning visual concepts for efficient and interpretable transfer learning,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Pruning visual concepts for efficient and interpretable transfer learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:30.618516Z

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 5f78a38c-9bf2-4bbc-bd50-5a9473fb77be · outbound

This paper cites Making users indistinguishable: Attribute-wise unlearning in recommender systems,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Making users indistinguishable: Attribute-wise unlearning in recommender systems,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:19:26.891050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec5e3fcf-0f40-4591-8b83-5672cc934f78 · outbound

This paper cites Post-Training Attribute Unlearning in Recommender Systems.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Post-Training Attribute Unlearning in Recommender Systems

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:19:28.728141Z

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 adfe4545-e6e5-4685-afe4-18157941fd80 · outbound

This paper cites Robust detection of distributed targets based on Rao test and Wald test,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Robust detection of distributed targets based on Rao test and Wald test,

Reference 19

Resolution: malformed identifier; no resolver link, observed 2026-08-06T23:19:27.116326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f51da8f5-014c-44d2-95b7-712d16c1cdd6 · outbound

This paper cites MEDIAN: Adaptive Intermediate-grained Aggregation Network for Composed Image Retrieval,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 MEDIAN: Adaptive Intermediate-grained Aggregation Network for Composed Image Retrieval,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:30.382069Z

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 846022c0-5724-43d0-86ad-688a283fbfd8 · outbound

This paper cites Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:19:28.476133Z

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 50672d79-59f0-4466-9216-c2d419311873 · outbound

This paper cites DDPM-MoCo: Advancing industrial surface defect generation and detection with generative and contrastive learning,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 DDPM-MoCo: Advancing industrial surface defect generation and detection with generative and contrastive learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:30.157428Z

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 a80d91e5-6b41-4a5d-95c3-a0ac93c51856 · outbound

This paper cites Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T23:19:27.522737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74c6a968-0825-4088-a72a-7a874b333409 · outbound

This paper cites DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:19:28.252517Z

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 cc89aba4-8199-42df-b73a-7ef8368eb850 · outbound

This paper cites Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:19:28.030399Z

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 2197e74a-9d45-4551-afe0-8256f73017d7 · outbound

This paper cites Research on brand strategy of hotel enterprises—taking Hyatt Hotel Group as an example,.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 Research on brand strategy of hotel enterprises—taking Hyatt Hotel Group as an example,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:29.938698Z

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 bda3248c-dfea-4c63-a6c2-64fb2c31e644 · outbound

This paper cites SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition.

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017 SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T23:19:26.662525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 42daf798-13ab-4843-af52-679bd25f2ff0 · inbound

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection cites this paper.

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T15:19:07.018781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.