MACHINE LEARNING TECHNIQUES FOR IDENTIFYING AND MITIGATING PHISHING ATTACKS
Keywords:
Security, Phishing Attacks, Machine LearningAbstract
One of the most prevalent forms of social engineering, phishing attempts to fraudulently get sensitive information from users' email accounts. Their usage can be integrated into larger-scale assaults aimed at penetrating government or business networks. To identify and lessen the impact of these attacks, several antiphishing methods have been suggested within the past ten years. Nevertheless, they continue to be inaccurate and inefficient. Many different channels can be used for phishing, including email, phone, instant messaging, advertisements, website pop-up windows, and DNS poisoning. Significant damages, such as the disclosure of sensitive information, theft of personal or company identities, or even state secrets, can be inflicted upon victims of phishing attempts. This essay aims to evaluate these attacks by looking at how phishing is done now and how it is currently perceived. This article presents a new, comprehensive model of phishing that considers various aspects of attacks, including stages, threats, targets, media, and tactics. Here, we use machine learning methods like Logistic Regression, Random Forest, and XGBoost to classify websites as either legitimate or phishing. In addition to helping readers understand the lifecycle of a phishing assault, the proposed anatomy will make people more aware of these attacks, the tactics used, and how to build a thorough anti-phishing system.
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Copyright (c) 2024 Venkata Sai Swaroop Reddy, Nallapa Reddy (Author)
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