Adversarial Attack Detection in Banking Networks Using Ensemble Learning and AI

Abstract

This research work explores the application of ensemble learning and artificial intelligence (AI) in detecting adversarial attacks in banking networks. Ensemble learning, a machine learning approach that combines the predictions of multiple models, can improve the accuracy and robustness of attack detection systems. AI-powered detection systems can analyze vast amounts of data in real-time to identify patterns and anomalies indicative of adversarial attacks. The article discusses the benefits and applications of ensemble learning and AI-powered detection in banking networks, including fraud detection, network security, and compliance. The authors recommend that banking institutions consider implementing these technologies to improve their cybersecurity posture and protect against evolving cyber threats.