AI-Powered Phishing Detection and Prevention Systems for Securing Financial Transactions in Industry 5.0
Year: 2025
Type: article
Abstract: Phishing attacks remain one of the most significant cybersecurity threats to financial transactions, especially in the evolving landscape of Industry 5.0, where interconnected systems and intelligent automation play a crucial role. This research presents an AI-powered phishing detection and prevention system that leverages advanced machine learning and deep learning algorithms, including Random Forest, Gradient Boosting Machines, BiLSTM, and Graph Neural Networks, to detect and mitigate phishing attempts in real-time. The proposed system analyzes various phishing vectors, such as email content, network traffic, and transactional anomalies, to enhance detection accuracy while minimizing false positives. Experimental results demonstrate an overall accuracy of 98.3% with a detection time of 95 milliseconds, ensuring real-time protection without disrupting legitimate financial transactions. By integrating AI-driven security mechanisms into financial ecosystems, this research contributes to strengthening cybersecurity defenses against sophisticated phishing threats. The study highlights the practical application of the proposed system in real-world scenarios, showcasing its effectiveness in reducing phishing-related incidents. Despite its high performance, challenges such as computational complexity and adaptability to evolving threats remain, requiring continuous model updates and improvements. This research underscores the importance of AI in securing financial transactions and provides a foundation for further advancements in phishing prevention strategies within Industry 5.0.
Authors: Shivaraj Yanamandram Kuppuraju -, Chandra Sekhar Dash -, Sambhav Patil -
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408104478