Title: Transforming Multidisciplinary Research Through Artificial Intelligence
Chief Editors: Dr. Nagasudha R and Dr. Geetha V
Associate Editors: Dr. Aarti Sharma and Ms. P. Nithyashankari
Co-Editors: Dr. Madhumathi Reddim and Dr. Shishira Srinivasa
ISBN: 978-93-7183-019-5
Chapter: 2
DOI: https://doi.org/10.59646/804/02
Author: Banumathi R
Abstract
Phishing is one of the most widespread cybersecurity threats, in which attackers attempt to deceive individuals or organizations into revealing confidential information such as passwords, banking credentials, authentication codes, and personal data. Traditional phishing detection systems primarily depend on manually created rules, blacklists, signatures, and static security policies. Although these approaches are useful, they may have difficulty detecting newly created phishing websites, sophisticated social-engineering messages, and rapidly changing attack patterns. Artificial Intelligence (AI) provides new opportunities to improve phishing detection by automatically learning patterns associated with malicious emails, websites, URLs, and user behavior. AI-powered phishing detection combines techniques from machine learning, deep learning, natural language processing, computer vision, and behavioral analysis. Machine-learning algorithms can analyze features such as URL structure, domain information, email content, hyperlinks, sender characteristics, and webpage properties to classify suspicious activities. Natural language processing can examine the linguistic characteristics of phishing messages, while deep-learning models can identify complex relationships that may not be easily detected through conventional rules. AI systems can also support real-time detection and adaptive security mechanisms. This chapter presents the fundamental concepts of phishing, limitations of traditional detection methods, the architecture of AI-powered phishing detection, important AI techniques, feature extraction, model training, evaluation metrics, applications, advantages, limitations, ethical considerations, and future research directions. The chapter also discusses how explainable and adaptive AI can improve user trust and strengthen cybersecurity defenses. AI should not be viewed as a complete replacement for conventional security mechanisms; rather, combining AI with established cybersecurity controls can provide a more comprehensive defense against evolving phishing attacks.
Keywords: Artificial Intelligence, Phishing Detection, Machine Learning, Deep Learning, Cybersecurity, Natural Language Processing, URL Analysis, Email Security, Social Engineering, Explainable AI.