论文标题
使用表格生成对抗网络的对抗性DDOS攻击的合成
Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks
论文作者
论文摘要
网络入侵检测系统（NIDS）是广泛用于维护计算机网络和信息系统的工具或软件，并防止恶意贩运者穿透它们，因为当有人试图闯入系统时，它们会标记。这些系统已经做出了最大的努力，到目前为止取得的结果非常令人满意，但是，随着攻击技术的不断发展，新型攻击脱颖而出，其中一次攻击是基于生成的对抗网络（GAN）的攻击，可以逃避机器学习ID，使它们容易受到伤害。该项目调查了使用使用gans在ID上产生的实际DDOS攻击合成的对抗攻击的影响。目的是发现这些系统将对合成攻击的反应。标记这些系统的脆弱性和弱点，以便我们可以修复它们。
Network Intrusion Detection Systems (NIDS) are tools or software that are widely used to maintain the computer networks and information systems keeping them secure and preventing malicious traffics from penetrating into them, as they flag when somebody is trying to break into the system. Best effort has been set up on these systems, and the results achieved so far are quite satisfying, however, new types of attacks stand out as the technology of attacks keep evolving, one of these attacks are the attacks based on Generative Adversarial Networks (GAN) that can evade machine learning IDS leaving them vulnerable. This project investigates the impact of the Adversarial Attacks synthesized using real DDoS attacks generated using GANs on the IDS. The objective is to discover how will these systems react towards synthesized attacks. marking the vulnerability and weakness points of these systems so we could fix them.
