Congratulations to our researcher Sebastián García for getting his paper "Deep Convolutional Neural Networks for DGA Detection" selected for the CCIS Springer book series! The publication Computer Science – CACIC 2018 (24th Argentine Congress, Tandil, Argentina, October 8–12, 2018, Revised Selected Papers) can be downloaded here.
Carlos Catania, Sebastian García, Pablo Torres
A Domain Generation Algorithm (DGA) is an algorithm to generate domain names in a deterministic but seemly random way. Malware use DGAs to generate the next domain to access the Command & Control (C&C) communication server. Given the simplicity of the generation process and speed at which the domains are generated, a fast and accurate detection method is required. Convolutional neural network (CNN) are well known for performing real-time detection in fields like image and video recognition. Therefore, they seemed suitable for DGA detection. The present work provides an analysis and comparison of the detection performance of a CNN for DGA detection. A CNN with a minimal architecture complexity was evaluated on a dataset with 51 DGA malware families and normal domains. Despite its simple architecture, the resulting CNN model correctly detected more than 97% of total DGA domains with a false positive rate close to 0.7%.