Paper Citation Record · LEDGER

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies

As of 16 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2412.12217.

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

pith.paper-citation-record.v1
2412.12217 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:03:56.351701Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:03:54.577559Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T21:26:48.694682Z

Reference resolution

56 of 56 outbound references displayed

verified exact2
verified fuzzy47
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c8fee6cf-20db-44ee-b68d-2018974ddf9a · outbound

This paper cites Botnet detection using r ecur- rent variational autoencoder,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Botnet detection using r ecur- rent variational autoencoder,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.402026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 973d196e-4d26-4c9e-b21f-340f6c3d4a64 · outbound

This paper cites A visualized botnet detection system based deep learning for the internet of things networks of smart cities ,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies A visualized botnet detection system based deep learning for the internet of things networks of smart cities ,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.375295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8ac221db-7f22-435e-ae7b-fd7442bcd769 · outbound

This paper cites Detecting DGA domains with recurrent neural net works and side information,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Detecting DGA domains with recurrent neural net works and side information,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.339253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 81d85dac-8612-4aa8-b25a-6ed49432f1c0 · outbound

This paper cites A LST M- based framework for handling multiclass imbalance in DGA bo tnet detection,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies A LST M- based framework for handling multiclass imbalance in DGA bo tnet detection,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.305540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5da54e93-8340-4221-9576-206ec63de417 · outbound

This paper cites Detecting st ealthy domain generation algorithms using heterogeneous deep neu ral network framework,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Detecting st ealthy domain generation algorithms using heterogeneous deep neu ral network framework,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.278554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2d7e19c1-8fc5-4224-bdfe-f3da29e40831 · outbound

This paper cites FGMD: A robust detector aga inst adversarial attacks in the IoT network,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies FGMD: A robust detector aga inst adversarial attacks in the IoT network,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.244518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd3223aa-f700-4b82-8171-f2ea45958e0e · outbound

This paper cites Adve rsarial attacks against network intrusion detection in IoT systems ,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adve rsarial attacks against network intrusion detection in IoT systems ,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.210982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c84b781b-740d-4239-913c-77229e8e375b · outbound

This paper cites Gene rative adversarial attacks against intrusion detection systems u sing active learning,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Gene rative adversarial attacks against intrusion detection systems u sing active learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.185131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f3f32801-4e0b-4db0-8dc7-8cc6f1ef3143 · outbound

This paper cites MAND A: On Adversarial Example Detection for Network Intrusion Det ection System,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies MAND A: On Adversarial Example Detection for Network Intrusion Det ection System,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.162597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 240b8f66-c336-4cf6-a0eb-6b3b9ed492c2 · outbound

This paper cites Biometric face presentation attack detection wit h multi-channel convolutional neural network,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Biometric face presentation attack detection wit h multi-channel convolutional neural network,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.137524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a7f8467f-d7b7-4f76-95e0-655b23af9bd4 · outbound

This paper cites Deep representations for iris, face, and ﬁnger- print spooﬁng detection,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Deep representations for iris, face, and ﬁnger- print spooﬁng detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.104826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 99697108-f06f-4439-ad07-cb9b7fa28bf3 · outbound

This paper cites Presentatio n attack detection using a tiny fully convolutional network,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Presentatio n attack detection using a tiny fully convolutional network,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.080605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8f8c0007-02c1-4852-8a20-eb5c0e0a149b · outbound

This paper cites Deep Boltzmann machines for robust ﬁngerprint spooﬁ ng attack detection,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Deep Boltzmann machines for robust ﬁngerprint spooﬁ ng attack detection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.036789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 62080924-5aaf-41a1-9b34-6056d9b755e8 · outbound

This paper cites Mob ile encrypted trafﬁc classiﬁcation using deep learning,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Mob ile encrypted trafﬁc classiﬁcation using deep learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:58.006653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 973cb4d3-e47f-4a6f-87ed-4f2dcd687ce7 · outbound

This paper cites Mitigating Challenges in Ethereum's Proof-of-Stake Consensus: Evaluating the Impact of EigenLayer and Lido.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Mitigating Challenges in Ethereum's Proof-of-Stake Consensus: Evaluating the Impact of EigenLayer and Lido

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T15:03:55.538455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4acacb3b-dcf6-4e96-95f6-6a96eb31f382 · outbound

This paper cites Multitask learning for network tr afﬁc classiﬁca- tion,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Multitask learning for network tr afﬁc classiﬁca- tion,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.975655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c14d0e38-134a-419f-902e-2fd172a9db4d · outbound

This paper cites Flowpic: Encrypted interne t trafﬁc classi- ﬁcation is as easy as image recognition,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Flowpic: Encrypted interne t trafﬁc classi- ﬁcation is as easy as image recognition,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.947885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ebbe9d84-6de9-4e71-a969-940fd5a9f752 · outbound

This paper cites Deep ﬁng erprinting: Undermining website ﬁngerprinting defenses with deep lear ning,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Deep ﬁng erprinting: Undermining website ﬁngerprinting defenses with deep lear ning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.919823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 74c09284-63a0-4fde-99f8-61739cdb37ef · outbound

This paper cites Adversarial examples: A survey and experimental eva luation of practical attacks on machine learning for windows malware d etection,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adversarial examples: A survey and experimental eva luation of practical attacks on machine learning for windows malware d etection,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.884164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 78c3a212-3f25-4e17-b579-49562d8f3135 · outbound

This paper cites DL-FHMC: Deep learning-b ased ﬁne-grained hierarchical learning approach for robust mal ware classiﬁ- cation,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies DL-FHMC: Deep learning-b ased ﬁne-grained hierarchical learning approach for robust mal ware classiﬁ- cation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.850793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2c97d575-1f33-4bf6-9874-a037de334e1a · outbound

This paper cites Soteria: Detecting adversarial ex amples in control ﬂow graph-based malware classiﬁers,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Soteria: Detecting adversarial ex amples in control ﬂow graph-based malware classiﬁers,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.792922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e4fa060c-44c1-4ee5-8cdf-37493cb680e2 · outbound

This paper cites DeepDGA: A dversarially- tuned domain generation and detection,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies DeepDGA: A dversarially- tuned domain generation and detection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.719101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 85ad48b7-80f3-4b15-9448-197a9394b7d7 · outbound

This paper cites FGMD: A robust detector ag ainst adversarial attacks in the IoT network,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies FGMD: A robust detector ag ainst adversarial attacks in the IoT network,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.662194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f84320ba-c97a-4b57-b3f4-343f20f2396f · outbound

This paper cites Tiki-taka: A ttacking and de- fending deep learning-based intrusion detection systems,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Tiki-taka: A ttacking and de- fending deep learning-based intrusion detection systems,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.539459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9f091c47-48f0-4a30-b98d-ac8d3d924030 · outbound

This paper cites Intriguing properties of neural networks.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Intriguing properties of neural networks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T15:03:55.869778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 323236d1-5aeb-440b-9220-d252c233243e · outbound

This paper cites Adversarial Perturbations Against Deep Neural Networks for Malware Classification.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adversarial Perturbations Against Deep Neural Networks for Malware Classification

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T15:03:55.906885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5febab5a-dd20-4b2b-89f7-e2c8b17ee655 · outbound

This paper cites Adversarial examples for malware detection,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adversarial examples for malware detection,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.304355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ecc187fc-ef30-4378-9f00-85dbf85cf185 · outbound

This paper cites COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:03:56.742515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e6c0a39f-2fa0-4e70-80a7-e0f9949ba82d · outbound

This paper cites Adversarial learning attacks on graph-based Io T malware detection systems,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adversarial learning attacks on graph-based Io T malware detection systems,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.823386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5e5335ae-0028-46c7-9129-5abf65fd9868 · outbound

This paper cites Securing malware cogn itive sys- tems against adversarial attacks,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Securing malware cogn itive sys- tems against adversarial attacks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.741340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b9cb4c5f-83f2-44d8-a88c-6cb841145b93 · outbound

This paper cites Dl-fhmc: Deep learning-b ased ﬁne-grained hierarchical learning approach for robust mal ware classiﬁ- cation,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Dl-fhmc: Deep learning-b ased ﬁne-grained hierarchical learning approach for robust mal ware classiﬁ- cation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.274856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 638c8133-6904-444f-9d00-4462454d8497 · outbound

This paper cites DeepDGA: A dversarially- tuned domain generation and detection,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies DeepDGA: A dversarially- tuned domain generation and detection,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.248511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 47e597ee-d050-494e-9f2f-80f62df7c16f · outbound

This paper cites CharBot: A simple and effective method for evading DGA classiﬁers,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies CharBot: A simple and effective method for evading DGA classiﬁers,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.692364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e7f8abf8-436f-4c2d-96ff-3c1bc7305f1a · outbound

This paper cites MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:03:56.605397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 92170547-6d1d-4ca0-8e67-65aec28ad2c2 · outbound

This paper cites Khaos: An adversarial neural network DGA with high anti-detection ability,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Khaos: An adversarial neural network DGA with high anti-detection ability,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.192798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ad2b7661-792e-4918-974b-32e99f93bd0e · outbound

This paper cites CLETer: A Charac ter- level Evasion Technique Against Deep Learning DGA Classiﬁe rs,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies CLETer: A Charac ter- level Evasion Technique Against Deep Learning DGA Classiﬁe rs,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.161690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f2830372-731c-4504-9faf-a2aa8d7b459a · outbound

This paper cites Demystifying the transferability of advers arial attacks in computer networks,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Demystifying the transferability of advers arial attacks in computer networks,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.106325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5136c3ef-504d-42d5-a2a3-9fb86542aa17 · outbound

This paper cites Zhang, S.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Zhang, S

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.060235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b9d14c91-7b64-41aa-876d-ce9fe4c6d95e · outbound

This paper cites Analyzing advers arial attacks against deep learning for intrusion detection in IoT networ ks,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Analyzing advers arial attacks against deep learning for intrusion detection in IoT networ ks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.578941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 01840edc-6bc7-49fc-85df-f04cb7f07be3 · outbound

This paper cites Tiki-taka: A ttacking and de- fending deep learning-based intrusion detection systems,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Tiki-taka: A ttacking and de- fending deep learning-based intrusion detection systems,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:56.993770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1e9def2a-f458-4ab7-92b2-769d98926337 · outbound

This paper cites Gen erative adversarial attacks against intrusion detection systems u sing active learning,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Gen erative adversarial attacks against intrusion detection systems u sing active learning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.604143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b98b3fb0-cc07-4b2c-9a83-043bc6b5136a · outbound

This paper cites Adv ersarial attacks against network intrusion detection in IoT systems ,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adv ersarial attacks against network intrusion detection in IoT systems ,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.626556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b3b20723-9281-422a-8db3-4b9053dec651 · outbound

This paper cites Adversarial attacks on remote user authentication using b ehavioural mouse dynamics,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adversarial attacks on remote user authentication using b ehavioural mouse dynamics,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.431613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 630e12b6-f525-4f45-abb0-87e8e2c57078 · outbound

This paper cites V o iceprint mimicry attack towards speaker veriﬁcation system in smart home,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies V o iceprint mimicry attack towards speaker veriﬁcation system in smart home,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.474553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f48ec2c6-b02d-45c1-b39e-242800a79b87 · outbound

This paper cites Attack on practical speaker veriﬁcation system using u niversal adversarial perturbations,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Attack on practical speaker veriﬁcation system using u niversal adversarial perturbations,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.498253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5cc65431-f0bd-402a-a9e5-99b3e972c935 · outbound

This paper cites Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T15:03:56.208005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88da0d1c-5a35-478d-8d42-560abd5f4ff1 · outbound

This paper cites Adversarial sample detection for speaker ve riﬁcation by neural vocoders,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adversarial sample detection for speaker ve riﬁcation by neural vocoders,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:56.961502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7218db5a-9250-4dfd-b31b-696e96cd153e · outbound

This paper cites Net- work trafﬁc obfuscation: An adversarial machine learning a pproach,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Net- work trafﬁc obfuscation: An adversarial machine learning a pproach,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.335699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e9be8ff9-af25-4006-b3cf-a38b0357d729 · outbound

This paper cites Black- box adversarial machine learning attack on network trafﬁc clas siﬁcation,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Black- box adversarial machine learning attack on network trafﬁc clas siﬁcation,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.359235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3e1ff4e4-d680-41dd-acef-02c73bd7043b · outbound

This paper cites Mocki ngbird: Defending against deep-learning-based website ﬁngerprin ting attacks with adversarial traces,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Mocki ngbird: Defending against deep-learning-based website ﬁngerprin ting attacks with adversarial traces,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:57.400289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0b3978aa-151d-4917-b66a-81c19643bd9a · outbound

This paper cites Attack versus attack : Toward adversarial example defend website ﬁngerprinting attack,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Attack versus attack : Toward adversarial example defend website ﬁngerprinting attack,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:56.905360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1328727f-304e-4e3c-85d6-33251b4c499a · outbound

This paper cites Adversar ial network trafﬁc: Towards evaluating the robustness of deep-learnin g-based net- work trafﬁc classiﬁcation,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Adversar ial network trafﬁc: Towards evaluating the robustness of deep-learnin g-based net- work trafﬁc classiﬁcation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:56.880711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e35685cb-ee19-48e6-84dc-7d24845e3e0d · outbound

This paper cites A survey of adversarial machine learning in c yber warfare,.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies A survey of adversarial machine learning in c yber warfare,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:03:56.850745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 36bd5e06-6d6a-4fd9-b367-61d8ce5e9bde · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Distilling the Knowledge in a Neural Network

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T15:03:56.322009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b81eced-ee77-4a8b-9779-bb1ed92f1fcb · outbound

This paper cites Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T15:03:56.337652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d8d4417-bd6f-467d-ac51-475d46476708 · outbound

This paper cites Strengthening DeFi Security: A Static Analysis Approach to Flash Loan Vulnerabilities.

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies Strengthening DeFi Security: A Static Analysis Approach to Flash Loan Vulnerabilities

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T15:03:56.351701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b8c2dd55-77a1-44ca-818e-5058e491f866 · inbound

Accelerating Sparse Graph Neural Networks with Tensor Core Optimization cites this paper.

Accelerating Sparse Graph Neural Networks with Tensor Core Optimization Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T15:03:54.577559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b49e0beb-8cb6-431e-8a35-970462f12ade · inbound

Blockchain-Based Secure Vehicle Auction System with Smart Contracts cites this paper.

Blockchain-Based Secure Vehicle Auction System with Smart Contracts Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:26:48.700113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.