Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis

• URL: http://arxiv.org/abs/2408.03441v1
• Date: Tue, 6 Aug 2024 20:40:20 GMT
• Title: Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis
• Authors: Ahod Alghureid, David Mohaisen,
• Abstract summary: We investigate the impact of various adversarial attack strategies on model performance metrics, such as accuracy, precision, recall, and F1-score. We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness.
• Score: 12.607077453567594
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper explores the vulnerability of machine learning models, specifically Random Forest, Decision Tree, and K-Nearest Neighbors, to very simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance metrics, such as accuracy, precision, recall, and F1-score. Our findings, highlighting how prone those techniques are to simple attacks, are alarming, and the inconsistency in the attacks' effect on different algorithms promises ways for attack mitigation. We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness.

Related papers

• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• Enhancing Adversarial Attacks: The Similar Target Method [6.293148047652131]
  adversarial examples pose a threat to deep neural networks' applications. Deep neural networks are vulnerable to adversarial examples, posing a threat to the models' applications and raising security concerns. We propose a similar targeted attack method named Similar Target(ST)
  arXiv  Detail & Related papers  (2023-08-21T14:16:36Z)
• Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning [80.21709045433096]
  A standard method in adversarial robustness assumes a framework to defend against samples crafted by minimally perturbing a sample. We use metric learning to frame adversarial regularization as an optimal transport problem. Our preliminary results indicate that regularizing over invariant perturbations in our framework improves both invariant and sensitivity defense.
  arXiv  Detail & Related papers  (2022-11-04T13:54:02Z)
• MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors [24.296350262025552]
  We propose a novel framework, called MEAD, for evaluating detectors based on several attack strategies. Among them, we make use of three new objectives to generate attacks. The proposed performance metric is based on the worst-case scenario.
  arXiv  Detail & Related papers  (2022-06-30T17:05:45Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples [107.38834819682315]
  We study a conceptually simple approach to defend few-shot classifiers against adversarial attacks. We propose a simple attack-agnostic detection method, using the concept of self-similarity and filtering. Our evaluation on the miniImagenet (MI) and CUB datasets exhibit good attack detection performance.
  arXiv  Detail & Related papers  (2021-10-24T05:46:03Z)
• Identification of Attack-Specific Signatures in Adversarial Examples [62.17639067715379]
  We show that different attack algorithms produce adversarial examples which are distinct not only in their effectiveness but also in how they qualitatively affect their victims. Our findings suggest that prospective adversarial attacks should be compared not only via their success rates at fooling models but also via deeper downstream effects they have on victims.
  arXiv  Detail & Related papers  (2021-10-13T15:40:48Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Poisoning Attacks on Algorithmic Fairness [14.213638219685656]
  We introduce an optimization framework for poisoning attacks against algorithmic fairness. We develop a gradient-based poisoning attack aimed at introducing classification disparities among different groups in the data. We believe that our findings pave the way towards the definition of an entirely novel set of adversarial attacks targeting algorithmic fairness in different scenarios.
  arXiv  Detail & Related papers  (2020-04-15T08:07:01Z)
• Feature Partitioning for Robust Tree Ensembles and their Certification in Adversarial Scenarios [8.300942601020266]
  We focus on evasion attacks, where a model is trained in a safe environment and exposed to attacks at test time. We propose a model-agnostic strategy that builds a robust ensemble by training its basic models on feature-based partitions of the given dataset. Our algorithm guarantees that the majority of the models in the ensemble cannot be affected by the attacker.
  arXiv  Detail & Related papers  (2020-04-07T12:00:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.