ENHANCING PHISHING DETECTION IN FINANCIAL INFORMATION SYSTEMS USING DEEP NEURAL NETWORKS: A REVIEW

Abstract

With the rapid advancement of digital banking and online financial transactions, phishing attacks have become a significant cyber security concern. This paper presents a comprehensive review of deep neural network (DNN)-based techniques for phishing detection in financial information systems. The study examines existing phishing detection methods, including traditional approaches and advanced machine learning techniques. A critical analysis of recent journal articles highlights key developments, challenges, and future directions in phishing detection. The review discusses the advantages of deep neural networks in enhancing detection accuracy, minimizing false positives, and improving real-time threat identification. Furthermore, the study identifies gaps in existing research and suggests potential improvements for future phishing detection frameworks.