Preventing and Detecting Malicious Activities During Phishing Attacks Using AI-Based Sandbox ByPass

Abstract

Phishing attacks are one of the most pervasive cyber threats that target both individuals and organizations. These attacks typically involve fraudulent communications, often masquerading as legitimate entities, to manipulate victims into revealing sensitive information such as login credentials, financial details, or personal data. This paper aims to explore the various forms of phishing attacks, including spear phishing, whaling, and clone phishing, and analyzes the methods employed by attackers to deceive their targets. By examining the human, technical, and social vulnerabilities exploited during these attacks, the study proposes effective strategies for preventing and detecting malicious activities associated with phishing. This includes the use of machine learning algorithms, anomaly detection systems, user awareness programs, and multi-layered security protocols. The paper highlights the importance of adaptive defense mechanisms and proactive threat detection systems to combat the increasing sophistication of phishing campaigns. It further emphasizes the need for continuous education and collaboration between organizations to develop a robust cybersecurity framework that can mitigate the risks posed by phishing attacks.