Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection
Background: Explainability in phishing detection model can support a further solution of phishing attack mitigation by increasing trust and understanding how phishing can be detected. Objective: The aims of this study to determine and best recommendation to apply an approach which has several components with abilities to fulfil the critical needs Methods: A methodology starting with analyzing both black-box and white-box models to get the pros and cons specifically in phishing detection. The conclusion of the analysis will be validated by experiment using a set of well-known algorithms and public phishing datasets. Experimental metrics covers 3 measurements such as predictive accuracy and explainability metrics. Conclusion: Both models are comparable in terms of interpretability and consistency, with room for improvement in diverse datasets. EBM as an example of white-box model is generally better suited for applications requiring explainability and actionable insights. Finally, each model, white-box and black-box model has positive and negative aspects both for performance metric and for explainable metric. It is important to consider the objective of model usage.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Detecting Phishing Sites -- An Overview
Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal in-formation. To minimize the damage caused by phishing…
PhishMatch: A Layered Approach for Effective Detection of Phishing URLs
Phishing attacks continue to be a significant threat on the Internet. Prior studies show that it is possible to determine whether a website is phishing or not just by analyzing its URL more carefully. A major advantage o…
Deep Learning-Based Framework for Phishing Website Detection
Phishing attackers spread phishing links through e-mail, text messages, and social media platforms. They use social engineering skills to trick users into visiting phishing websites and entering crucial personal inform…
Deep LearningPhishing Website DetectionPredictionAdvanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers
Machine learning (ML) based approaches have been the mainstream solution for anti-phishing detection. When they are deployed on the client-side, ML-based classifiers are vulnerable to evasion attacks. However, such poten…
Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks
The URL components of web addresses are frequently used in creating phishing detection techniques. Typically, machine learning techniques are widely used to identify anomalous patterns in URLs as signs of possible phishi…
BIG-bench Machine Learning