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.

翻译：本文提出了一种创新的Attention-GAN框架，旨在增强网络安全，重点关注异常检测。针对网络威胁不断演变所带来的挑战，该方法通过生成多样且逼真的合成攻击场景，从而丰富数据集并提升威胁识别能力。将注意力机制与生成对抗网络（GANs）相结合是该方法的核心理念。注意力机制增强了模型聚焦相关特征的能力，这对于检测细微且复杂的攻击模式至关重要。此外，GANs通过生成额外多样化的攻击数据（涵盖已知和新兴威胁）解决了数据稀缺问题。这种双重方法确保了系统能够持续有效应对不断演变的网络攻击。该模型在KDD Cup和CICIDS2017数据集上进行了验证，在异常检测方面表现出显著提升。在KDD数据集上达到99.69%的准确率，在CICIDS2017数据集上达到97.93%的准确率，精确率、召回率和F1分数均超过97%，证明了其在识别复杂攻击模式方面的有效性。本研究通过为复杂动态网络威胁下的异常检测提供可扩展且自适应的解决方案，对网络安全领域做出了重要贡献。将GANs用于数据增强的探索，为未来研究指明了有前景的方向，尤其在数据限制阻碍网络安全系统发展的场景中。Attention-GAN框架已成为一种开创性方法，为先进的网络防御策略树立了新标杆。