A Privacy-Preserved Variational-Autoencoder for DGA Identification in the Education Industry and Distance Learning

One of the most insidious methods of bypassing security mechanisms in a modern information system is the domain generation algorithms (DGAs), which are used to disguise the identity of malware by periodically switching the domain name assigned to a command and control (C&C) server. Combating advanced techniques, such as DGAs, is an ongoing challenge that security organizations often need to work with and possibly share private data to train better and more up-to-date machine learning models. This logic raises serious concerns about data integrity, trade-related issues, and strict privacy protocols that must be adhered to. To address the concerns regarding the privacy and security of private data, we propose in this work a privacy-preserved variational-autoencoder to DGA combined with case studies from the education industry and distance learning, specifically because the recent pandemic has brought an explosive increase to remote learning. This is a system that, using the secured multi-party computation (SMPC) methodology, can successfully apply machine learning techniques, specifically the Siamese variational-autoencoder algorithm, on encrypted data and metadata. The method proposed for the first time in the literature facilitates learning specialized extraction functions of useful intermediate representations in complex deep learning architectures, producing improved training stability, high generalization performance, and remarkable categorization accuracy.