Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models
<|reference_start|>Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models: 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.<|reference_end|>
@article{ji2024evaluating,
    title={Evaluating the Effectiveness and Robustness of Visual Similarity-based
  Phishing Detection Models},
    author={Fujiao Ji, Kiho Lee, Hyungjoon Koo, Wenhao You, Euijin Choo,
  Hyoungshick Kim, and Doowon Kim},
    journal={arXiv preprint arXiv:2405.19598},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19598},
    primaryClass={cs.CR}
}