Privacy-Preserving Generative Adversarial Network-Based Data Synthesis for Intelligent Intrusion Detection Systems

With the development of the ICT industry, endpoint security issues are emerging. To quickly detect and respond to evolving attacks, it is necessary to improve threat detection performance without degrading endpoints’ performance. Conventional data learning techniques have the problem of severe performance degradation when learning unbalanced and sparse datasets. In this paper, we propose a privacy-preserving generative adversarial network (PP-GAN) technique to solve the problems of conventional methods. In addition, we minimize damage caused by dataset leakage by filtering out privacy features that may cause information leakage in advance. According to the experimental results, even if the dataset is unbalanced and sparse, we improved the accuracy by 29% compared to the model that did not apply GAN by augmenting the dataset with privacy data deleted, achieving an average attack classification accuracy of 75.75%. In addition, we reduced data leakage by about 10% while reducing the accuracy by 0.55% compared to the general GANbased dataset generation model.