Intrusion Detection System After Data Augmentation Schemes Based on the VAE and CVAE

Abstract

Industrial Internet of Things (IoT) is the most rapidly developing industry in the current IoT industry, and the intrusion detection system (IDS) remains one of the key technologies for industrial IoT security protection. Researchers have considered applying algorithms such as machine learning and deep learning to network IDSs to cope with complex and changing network environments and to automatically extract key features from high-dimensional feature data. However, in the real industrial IoT environment, data imbalance is the main factor that affects the performance of the deep-learning-based IDS. In this article, we study the network intrusion detection model based on data level. Three data-based research schemes are constructed step by step in this article, which are a data augmentation scheme based on the variational autoencoder (VAE), a data-balancing scheme based on the conditional VAE, and a data-balancing scheme based on random undersampling and conditional VAE. The three data-level-based schemes are combined with the deep-learning-based IDS. In this article, we build experiments based on the CSE-CIC-IDS2018 dataset to verify the effectiveness of three data processing schemes. After data enhancement through the third scheme, the Macro-F1-score of the convolutional-neural-network-based IDS model improved by 3.75% and the Macro-F1-score of the gated-recurrent-unit-based IDS model improved by 5.32%.