AI-Powered Predictive Cybersecurity in Identifying Emerging Threats through Machine Learning
Ruibin Wang · 2024
With an emphasis on the incorporation of the Wasserstein Generative Adversarial Network (WGAN) algorithm, this study examines the revolutionary potential of AI-Powered Predictive Cybersecurity in detecting new risks using machine learning. The adoption of an innovative cybersecurity approach within CyberGuard Bank is investigated in this paper through the use of a fictitious case study set in the financial sector. With the use of cutting-edge machine learning techniques and WGAN-generated synthetic data, the model exhibits improved threat detection, false positive reduction, real-time threat mitigation, and scalability capabilities. The implementation places a strong emphasis on ethical issues, including as bias prevention and privacy compliance, which positions the company as a responsible steward of AI-driven cybersecurity. The results highlight the strategic importance of WGAN and AI in strengthening defences against the constantly changing and dynamic array of cyberthreats.