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注意力生成对抗网络（Attention-GAN）框架，用于增强网络安全，重点关注异常检测。针对网络威胁不断演变所带来的挑战，所提方法旨在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，生成对抗网络通过生成额外的多样化攻击数据（涵盖已知及新兴威胁）来解决数据稀缺问题。这种双重方法确保系统能够持续有效应对不断演变的网络攻击。本文采用KDD Cup和CICIDS2017数据集对该模型进行验证，结果表明其在异常检测方面取得了显著提升。该模型在KDD数据集上实现了99.69%的准确率，在CICIDS2017数据集上实现了97.93%的准确率，且精确率、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。本研究通过为应对复杂多变的网络威胁提供一种可扩展且适应性强的异常检测解决方案，对网络安全领域具有重要意义。将生成对抗网络用于数据增强的探索为未来研究指明了有前景的方向，尤其是在数据限制制约网络安全系统发展的情境下。注意力生成对抗网络框架成为了一种开创性方法，为先进的网络防御策略设立了新的标杆。