Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models

• URL: http://arxiv.org/abs/2405.19598v1
• Date: Thu, 30 May 2024 01:28:36 GMT
• Title: Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models
• Authors: Fujiao Ji, Kiho Lee, Hyungjoon Koo, Wenhao You, Euijin Choo, Hyoungshick Kim, Doowon Kim,
• Abstract summary: We comprehensively scrutinize and evaluate state-of-the-art visual similarity-based anti-phishing models. Our analysis reveals that while certain models maintain high accuracy, others exhibit notably lower performance than results on curated datasets. To the best of our knowledge, this work represents the first large-scale, systematic evaluation of visual similarity-based models for phishing detection in real-world settings.
• Score: 10.334870703744498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual similarity-based detection systems have emerged as an effective countermeasure, but their effectiveness and robustness in real-world scenarios have been unexplored. In this paper, we comprehensively scrutinize and evaluate state-of-the-art visual similarity-based anti-phishing models using a large-scale dataset of 450K real-world phishing websites. Our analysis reveals that while certain models maintain high accuracy, others exhibit notably lower performance than results on curated datasets, highlighting the importance of real-world evaluation. In addition, we observe the real-world tactic of manipulating visual components that phishing attackers employ to circumvent the detection systems. To assess the resilience of existing models against adversarial attacks and robustness, we apply visible and perturbation-based manipulations to website logos, which adversaries typically target. We then evaluate the models' robustness in handling these adversarial samples. Our findings reveal vulnerabilities in several models, emphasizing the need for more robust visual similarity techniques capable of withstanding sophisticated evasion attempts. We provide actionable insights for enhancing the security of phishing defense systems, encouraging proactive actions. To the best of our knowledge, this work represents the first large-scale, systematic evaluation of visual similarity-based models for phishing detection in real-world settings, necessitating the development of more effective and robust defenses.

Related papers

• A Survey and Evaluation of Adversarial Attacks for Object Detection [11.48212060875543]
  Deep learning models excel in various computer vision tasks but are susceptible to adversarial examples-subtle perturbations in input data that lead to incorrect predictions. This vulnerability poses significant risks in safety-critical applications such as autonomous vehicles, security surveillance, and aircraft health monitoring.
  arXiv  Detail & Related papers  (2024-08-04T05:22:08Z)
• 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)
• Principles of Designing Robust Remote Face Anti-Spoofing Systems [60.05766968805833]
  This paper sheds light on the vulnerabilities of state-of-the-art face anti-spoofing methods against digital attacks. It presents a comprehensive taxonomy of common threats encountered in face anti-spoofing systems.
  arXiv  Detail & Related papers  (2024-06-06T02:05:35Z)
• A Sophisticated Framework for the Accurate Detection of Phishing Websites [0.0]
  Phishing is an increasingly sophisticated form of cyberattack that is inflicting huge financial damage to corporations throughout the globe. This paper proposes a comprehensive methodology for detecting phishing websites. A combination of feature selection, greedy algorithm, cross-validation, and deep learning methods have been utilized to construct a sophisticated stacking ensemble.
  arXiv  Detail & Related papers  (2024-03-13T14:26:25Z)
• Improving the Robustness of Object Detection and Classification AI models against Adversarial Patch Attacks [2.963101656293054]
  We analyze attack techniques and propose a robust defense approach. We successfully reduce model confidence by over 20% using adversarial patch attacks that exploit object shape, texture and position. Our inpainting defense approach significantly enhances model resilience, achieving high accuracy and reliable localization despite the adversarial attacks.
  arXiv  Detail & Related papers  (2024-03-04T13:32:48Z)
• Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications [58.06882713631082]
  Deep neural networks exhibit excellent performance in computer vision tasks, but their vulnerability to real-world adversarial attacks raises serious security concerns. This paper proposes an efficient attention-based defense mechanism that exploits adversarial channel-attention to quickly identify and track malicious objects in shallow network layers. It also introduces an efficient multi-frame defense framework, validating its efficacy through extensive experiments aimed at evaluating both defense performance and computational cost.
  arXiv  Detail & Related papers  (2023-11-19T00:47:17Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• On the Robustness of Quality Measures for GANs [136.18799984346248]
  This work evaluates the robustness of quality measures of generative models such as Inception Score (IS) and Fr'echet Inception Distance (FID) We show that such metrics can also be manipulated by additive pixel perturbations.
  arXiv  Detail & Related papers  (2022-01-31T06:43:09Z)
• On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving [59.33715889581687]
  The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks. This paper presents an evaluation of the robustness of semantic segmentation models when attacked with different types of adversarial patches. A novel loss function is proposed to improve the capabilities of attackers in inducing a misclassification of pixels.
  arXiv  Detail & Related papers  (2022-01-05T22:33:43Z)
• Detection Defense Against Adversarial Attacks with Saliency Map [7.736844355705379]
  It is well established that neural networks are vulnerable to adversarial examples, which are almost imperceptible on human vision. Existing defenses are trend to harden the robustness of models against adversarial attacks. We propose a novel method combined with additional noises and utilize the inconsistency strategy to detect adversarial examples.
  arXiv  Detail & Related papers  (2020-09-06T13:57:17Z)

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.