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With the recent developments in artificial intelligence and machine learning,anomalies in network traffic can be detected using machine learning approaches.Before the rise of machine learning, network anomalies which could imply anattack, were detected using well-crafted rules. An attacker who has knowledgein the field of cyber-defence could make educated guesses to sometimesaccurately predict which particular features of network traffic data thecyber-defence mechanism is looking at. With this information, the attacker cancircumvent a rule-based cyber-defense system. However, after the advancementsof machine learning for network anomaly, it is not easy for a human tounderstand how to bypass a cyber-defence system. Recently, adversarial attackshave become increasingly common to defeat machine learning algorithms. In thispaper, we show that even if we build a classifier and train it with adversarialexamples for network data, we can use adversarial attacks and successfullybreak the system. We propose a Generative Adversarial Network(GAN)basedalgorithm to generate data to train an efficient neural network basedclassifier, and we subsequently break the system using adversarial attacks.

NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion