arXiv:2611.02312 · v1
Improving Financial Cybersecurity Through Fraud Transaction Detection with Deep Learning
摘要
Financial fraud detection remains a critical challenge in cybersecurity, with traditional rule-based systems struggling to keep pace with evolving attack patterns. This paper presents a deep learning approach for fraud transaction detection, leveraging neural network architectures to identify anomalous financial behaviors with high accuracy. We evaluate multiple deep learning models including LSTM, CNN, and hybrid architectures on real-world financial transaction datasets, demonstrating significant improvements over conventional machine learning baselines. Our approach achieves robust performance in detecting fraudulent transactions while minimizing false positives, providing a scalable solution for enterprise financial cybersecurity systems
作者备注：6 pages. Published in Proceedings of IEEE International Conference on Artificial Intelligence and Systems (AIS 2026), Texas A&M University, 2026
期刊信息：Proceedings of IEEE AIS 2026 (2026)