Evolving strategies in anti-phishing: an in-depth analysis of detection techniques and future research directions

Abstract

Phishing attacks are a major digital threat, impacting individuals and organizations globally. This review paper examines evolving anti-phishing strategies by analyzing five key techniques: URL blacklists, visual similarity detection, heuristic methods, machine learning models, and deep learning techniques. Each technique is evaluated for its mechanisms, unique features, and challenges. A systematic literature survey (SLR) is conducted to compare these methods; effectiveness. The paper highlights significant research challenges and suggests future directions, emphasizing the integration of artificial intelligence and behavioral analytics to combat evolving phishing tactics, this study aims to advance understanding and inspire more effective anti-phishing solutions.