Sebastián García in CCIS Springer book series

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.

Deep Convolutional Neural Networks for DGA Detection

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%.