生成对抗网络在网络安全中应用综述：前景、方向与开放研究范围
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes
AI总结：
本文综述了生成对抗网络（GANs）在网络安全中的应用，涵盖入侵检测、恶意软件检测等领域，讨论了挑战并展望了未来研究方向。
摘要：
随着人工智能的普及，需要以数字方式积累和传播的数据量大幅增加。由于数据在具有复杂和精密基础设施的数字环境中在线可用，因此基于网络安全实施各种防御机制至关重要。生成对抗网络（GANs）作为深度学习模型，已成为解决不断变化的安全问题的强大解决方案。本综述研究了深度学习模型（特别是GANs）在加强网络安全防御方面的重要性。我们的综述旨在探索GANs中已完成的各种工作，如入侵检测系统（IDS）、移动和网络入侵、僵尸网络检测和恶意软件检测。重点是研究GANs如何在这些领域成为加强网络安全防御的有影响力的工具。此外，本文讨论了在这些领域使用GANs的挑战和限制，并提出了未来的研究方向。总体而言，本文强调了GANs在增强网络安全措施方面的潜力，并指出了在该领域进一步探索的必要性。
英文摘要：
With the proliferation of Artificial Intelligence, there has been a massive increase in the amount of data required to be accumulated and disseminated digitally. As the data are available online in digital landscapes with complex and sophisticated infrastructures, it is crucial to implement various defense mechanisms based on cybersecurity. Generative Adversarial Networks (GANs), which are deep learning models, have emerged as powerful solutions for addressing the constantly changing security issues. This survey studies the significance of the deep learning model, precisely on GANs, in strengthening cybersecurity defenses. Our survey aims to explore the various works completed in GANs, such as Intrusion Detection Systems (IDS), Mobile and Network Trespass, BotNet Detection, and Malware Detection. The focus is to examine how GANs can be influential tools to strengthen cybersecurity defenses in these domains. Further, the paper discusses the challenges and constraints of using GANs in these areas and suggests future research directions. Overall, the paper highlights the potential of GANs in enhancing cybersecurity measures and addresses the need for further exploration in this field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
