Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks

Abstract--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. 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. Unsupervised Machine Learning, IDS systems can predict the attacks that aren't labeled but that techniques are prone to 1-I False positives [3], this gives the attackers the chance to Cyber Attacks are increasingly sophisticated, hackers keep mislead models into their desired misclassification by using adapting their strategies to exploit every possible vulnerability adversarial examples.