GAN and Explainable AI (XAI) for Cybersecurity in Healthcare

Abstract

In the rapid growth of cybersecurity, the development of artificial intelligence models, especially for vulnerable source code, has become indispensable. Over the past few years, an increased number of network attacks has significantly affected the regular functioning of social networks. To enhance the capabilities and interpretability of cybersecurity systems, generative adversarial networks (GANs) and explainable artificial intelligence (XAI) technologies are integrated for developing an effective fusion model. GANs enhance anomaly detection by generating synthetic data for model training and simulating adversarial scenarios for proactive defense. They play a crucial role in threat intelligence by generating synthetic threats for analysis. Explainable artificial intelligence (XAI) in cybersecurity focuses on making the decision-making processes of AI models transparent and understandable to human operators. It aims to provide insights into how AI models reach conclusions, building trust, reducing alert fatigue, and ensuring compliance with regulations. XAI enhances the interpretability of GAN-generated outputs, providing human-readable explanations for detected anomalies and simulated threats. The integration of GAN and XAI addresses challenges such as adversarial attacks and bias detection and supports continuous learning for adaptive cybersecurity measures.

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