  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 features, essential for detecting subtle and
complex attack patterns. In addition, GANs address the issue of data scarcity
by generating additional varied attack data, encompassing known and emerging
threats. This dual approach ensures that the system remains relevant and
effective against the continuously evolving cyberattacks. The KDD Cup and
CICIDS2017 datasets were used to validate this model, which exhibited
significant improvements in anomaly detection. It achieved an accuracy of
99.69% on the KDD dataset and 97.93% on the CICIDS2017 dataset, with precision,
recall, and F1-scores above 97%, demonstrating its effectiveness in recognizing
complex attack patterns. This study contributes significantly to cybersecurity
by providing a scalable and adaptable solution for anomaly detection in the
face of sophisticated and dynamic cyber threats. The exploration of GANs for
data augmentation highlights a promising direction for future research,
particularly in situations where data limitations restrict the development of
cybersecurity systems. The attention-GAN framework has emerged as a pioneering
approach, setting a new benchmark for advanced cyber-defense strategies.
