Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management

arXiv:2402.15945v1 Announce Type: new
Abstract: This paper proposes an innovative Attention-GAN framework for enhancing cybersecurity, focusing on anomaly detection. In response to the challenges posed by the constantly evolving nature of cyber threats, the proposed approach aims to generate diverse and realistic synthetic attack scenarios, thereby enriching the dataset and improving threat identification. Integrating attention mechanisms with Generative Adversarial Networks (GANs) is a key feature of the proposed method. The attention mechanism enhances the model's ability to focus on relevant …

anomaly detection arxiv attack attention challenges cs.ai cs.cr cutting cyber cybersecurity cybersecurity threat cyber threats dataset detection edge evolving nature framework gan management nature response synthetic threat threat management threats