Abstract

Despite advancements in security measures, phishing attacks remain a pervasive and sophisticated challenge posing a substantial risk to individuals and organizations. This paper explores a diverse approach to counter phishing cybercrime, emphasizing shortcomings in existing solutions for email users. Conducting a literature analysis on phishing mitigation methods, it addresses their limitations and concludes with an exploration of potential enhancements for more effective phishing detection in emails and websites. This paper critically ananlyzes the ethical considerations associated with machine learning in cybersecurity, emphasizing the importance of privacy and responsible use of data. The implications of adversarial attacks on machine learning models, emphasizing the need for robust and resilient systems in the ever-changing landscape of cyber threats are discussed. This paper delves into the multi-layered deception employed by phishers, offering a nuanced roadmap for understanding, anticipating, and ultimately thwarting them.