Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks
This addresses security vulnerabilities in network intrusion detection systems for cybersecurity, but it is incremental as it applies existing GAN methods to a new attack scenario.
The paper investigates the impact of adversarial DDoS attacks synthesized using tabular GANs on network intrusion detection systems, finding that these attacks can evade machine learning IDSs, highlighting vulnerabilities.
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.