Mitigating Bias in Machine Learning Models for Phishing Webpage Detection

• URL: http://arxiv.org/abs/2401.08363v1
• Date: Tue, 16 Jan 2024 13:45:54 GMT
• Title: Mitigating Bias in Machine Learning Models for Phishing Webpage Detection
• Authors: Aditya Kulkarni, Vivek Balachandran, Dinil Mon Divakaran, Tamal Das,
• Abstract summary: Phishing, a well-known cyberattack, revolves around the creation of phishing webpages and the dissemination of corresponding URLs. Various techniques are available for preemptively categorizing zero-day phishing URLs by distilling unique attributes and constructing predictive models. This proposal delves into persistent challenges within phishing detection solutions, particularly concentrated on the preliminary phase of assembling comprehensive datasets. We propose a potential solution in the form of a tool engineered to alleviate bias in ML models.
• Score: 0.8050163120218178
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The widespread accessibility of the Internet has led to a surge in online fraudulent activities, underscoring the necessity of shielding users' sensitive information from cybercriminals. Phishing, a well-known cyberattack, revolves around the creation of phishing webpages and the dissemination of corresponding URLs, aiming to deceive users into sharing their sensitive information, often for identity theft or financial gain. Various techniques are available for preemptively categorizing zero-day phishing URLs by distilling unique attributes and constructing predictive models. However, these existing techniques encounter unresolved issues. This proposal delves into persistent challenges within phishing detection solutions, particularly concentrated on the preliminary phase of assembling comprehensive datasets, and proposes a potential solution in the form of a tool engineered to alleviate bias in ML models. Such a tool can generate phishing webpages for any given set of legitimate URLs, infusing randomly selected content and visual-based phishing features. Furthermore, we contend that the tool holds the potential to assess the efficacy of existing phishing detection solutions, especially those trained on confined datasets.

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