Abstract
Phishing attacks are a major cybersecurity threat, tricking people with fake emails, scam websites, and social engineering tactics. As these attacks become more advanced, traditional security measures are no longer enough to stop them. This paper looks at how Artificial Intelligence (AI) can help detect and prevent phishing while also making people more aware of these threats. Using machine learning (ML), natural language processing (NLP), and behavioral analysis, AI can examine email content, sender behavior, and metadata to spot phishing attempts. AI-powered cybersecurity training can also teach people to recognize and respond to phishing by using personalized phishing tests and tracking user behavior to find those most at risk. This paper is based on a review of existing research on AI-driven phishing detection and human factors in cybersecurity. It brings together information from academic studies, industry reports, and case studies to explore how AI helps stop phishing and what challenges it faces. However, this study has some limitations since it relies only on past research and does not include new experiments or firsthand testing of AI security tools. Also, while AI can improve phishing detection, its success depends on the quality of training data and how attackers change their tactics. This paper concludes that the best way to improve cybersecurity is by combining AI-based security with user education and strong security policies.
Faculty Advisor/Mentor
Jeremiah Still
Document Type
Paper
Disciplines
Artificial Intelligence and Robotics | Cybersecurity
DOI
10.25776/kvys-9v37
Publication Date
3-22-2025
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Leveraging Artificial Intelligence to Strengthen Human Resilience Against Phishing Attacks
Phishing attacks are a major cybersecurity threat, tricking people with fake emails, scam websites, and social engineering tactics. As these attacks become more advanced, traditional security measures are no longer enough to stop them. This paper looks at how Artificial Intelligence (AI) can help detect and prevent phishing while also making people more aware of these threats. Using machine learning (ML), natural language processing (NLP), and behavioral analysis, AI can examine email content, sender behavior, and metadata to spot phishing attempts. AI-powered cybersecurity training can also teach people to recognize and respond to phishing by using personalized phishing tests and tracking user behavior to find those most at risk. This paper is based on a review of existing research on AI-driven phishing detection and human factors in cybersecurity. It brings together information from academic studies, industry reports, and case studies to explore how AI helps stop phishing and what challenges it faces. However, this study has some limitations since it relies only on past research and does not include new experiments or firsthand testing of AI security tools. Also, while AI can improve phishing detection, its success depends on the quality of training data and how attackers change their tactics. This paper concludes that the best way to improve cybersecurity is by combining AI-based security with user education and strong security policies.