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  • Evading Anti-Phishing Models: A Field Note Documenting an Experience in the Machine Learning Security Evasion Competition 2022
Title: Evading Anti-Phishing Models: A Field Note Documenting an Experience in the Machine Learning Security Evasion Competition 2022
Although machine learning-based anti-phishing detectors have provided promising results in phishing website detection, they remain vulnerable to evasion attacks. The Machine Learning Security Evasion Competition 2022 (MLSEC 2022) provides researchers and practitioners with the opportunity to deploy evasion attacks against anti-phishing machine learning models in real-world settings. In this field note, we share our experience participating in MLSEC 2022. We manipulated the source code of ten phishing HTML pages provided by the competition using obfuscation techniques to evade anti-phishing models. Our evasion attacks employing a benign overlap strategy achieved third place in the competition with 46 out of a potential 80 points. The results of our MLSEC 2022 performance can provide valuable insights for research seeking to robustify machine learning-based anti-phishing detectors.  more » « less
Award ID(s):
2338479 2319325 1946537 1917117
PAR ID:
10611546
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACM Digital Threats: Research and Practice
Date Published:
Journal Name:
Digital Threats: Research and Practice
Volume:
5
Issue:
1
ISSN:
2692-1626
Page Range / eLocation ID:
1 to 8
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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