Publication

Abstract : Increasing amount of cyber-attacks in past few years have necessitated the development of automatic threat detection systems for ensuring network and data security. Such automated threat detection models for handling cyber threats are subjected to adversarial attacks. Developing such robust detection models resilient to the attacks require rigorous training and testing with ample number of adversarial examples (AEs) prior to deployment in real-world scenarios where the models are subjected tothe new and unseen data and attacks. Generating adversarial examples with Generative Adversarial Networks (GANs) and its variants is a widely adopted technique. While many notable works generate adversarial examples using the confidence scores of the features that require extensive knowledge about the underlying features of the dataset. This work focuses on generating adversarial examples using Wasserstein condition generative adversarial network and analyzing the robustness of several Gradient-boosted classifiers namely, Adaptive boosting classifier, Categorical Boosting Classifier and Light gradient-boosting classifier on the synthetic and real data. A comparison is also made in the process of generating synthetic data with both GANs and Confidence scores on benchmark dataset NSL-KDD. The robustness of Gradient-boosted classifiers Adaboost, Catboost and LightGBM are tested with these generated data that claim for the superiority of variants of GAN over Confidence scores using features.