Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations | Paper Citation Record

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Arora, Yizhen Wang, Fei Wang, Hao Yang, and David Mohaisen

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

Reference 6

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This paper cites Dissecting the Infrastructure Used in Web-based Cryptojacking: A Measurement Perspective.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Dissecting the Infrastructure Used in Web-based Cryptojacking: A Measurement Perspective

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

Reference 8

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This paper cites Sanjalawe, and Selvakumar Manickam.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Sanjalawe, and Selvakumar Manickam

Reference 9

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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This paper cites Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis

Reference 11

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This paper cites Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks

Reference 12

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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This paper cites Duchi, and Percy Liang.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Duchi, and Percy Liang

Reference 14

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

Reference 17

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This paper cites RobustBench: a standardized adversarial robustness benchmark.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations RobustBench: a standardized adversarial robustness benchmark

Reference 18

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing

Reference 22

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Explaining and Harnessing Adversarial Examples

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Al-Ani, and Shankar Karuppayah

Reference 25

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

Reference 27

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients

Reference 29

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

Reference 31

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks

Reference 32

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Oliveira, Júlia Almeida Valadares, José Eduardo de Azevedo Sousa, Alex Borges Vieira, Heder Soares Bernardino, Saulo Moraes Villela, and Glauber Dias Gonçalves

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami

Reference 36

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Parizi, and Ali Dehghantanha

Reference 37

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

Reference 38

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Analyzing In-browser Cryptojacking

Reference 39

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Kamhoua, Joongheon Kim, DaeHun Nyang, and Aziz Mohaisen

Reference 40

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Kamhoua, Sachin Shetty, DaeHun Nyang, and David Mohaisen

Reference 41

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Sanjalawe and Salam Al-Emari

Reference 42

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Leslie, Charles A

Reference 43

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This paper cites Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

Reference 44

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Goodfellow, and Rob Fergus

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Yuille, and Kaiming He

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

Reference 56

Resolution: verified exact; raw_fallback, observed 2026-08-16T10:37:26.312916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e54cd29c-5ea2-476d-9d61-c36f35e1ed9d · outbound

This paper cites Bruse, Mikel Galar, and Raul Orduna Urrutia.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Bruse, Mikel Galar, and Raul Orduna Urrutia

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:25.948415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9acba289-3c27-4e48-ae1e-1b90559d8b9d · outbound

This paper cites Intriguing properties of neural networks.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Intriguing properties of neural networks

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:25.915054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 017f2840-7eeb-45e3-972e-f6ac10b95ecd · outbound

This paper cites In 31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations In 31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:25.861958Z

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Unavailable: canonical work link unavailable.

Observation ee5e30bd-0274-4306-9af2-ee598f0f4bf5 · outbound

This paper cites In Proceedings of the Workshop on Artificial Intelligence Safety 2021 (SafeAI 2021) co-located with the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI, Vol.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations In Proceedings of the Workshop on Artificial Intelligence Safety 2021 (SafeAI 2021) co-located with the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI, Vol

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:27.829589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d7eb39e3-ebe2-40e2-94a5-97649d72a35b · outbound

This paper cites an unresolved cited work.

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Unresolved cited work

Reference 2024

Resolution: metadata mismatch; raw_fallback, observed 2026-08-16T10:37:26.871255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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