Inline DGA Detection with Deep Networks

• Published in: IEEE ... International Conference on Data Mining workshops pp. 683 - 692
• Main Authors: Bin Yu, Gray, Daniel L., Pan, Jie, De Cock, Martine, Nascimento, Anderson C. A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2017
• Subjects: Data Mining; Deep Learning; DGA Detection; Feature extraction; IP networks; Machine learning; Malware; Neural networks; Servers; Training
• ISSN: 2375-9259
• DOI: 10.1109/ICDMW.2017.96

Domain generation algorithms (DGAs) automatically generate large numbers of domain names in DNS domain fluxing for the purpose of command-and-control (C&C) communication. DGAs are immune to static prevention methods like blacklisting and sinkholing. Detection of DGAs in a live stream of queries in a DNS server is referred to as inline detection. Most of the previous approaches in the literature on DGA detection either: (i) are based on small synthetic data sets for training, rather than data collected from real traffic or (ii) require contextual information and therefore cannot be used for inline detection. In this work, we overcome these limitations by proposing a novel way to label a large volume of data collected from real traffic as DGA/non-DGA and by using deep learning techniques. Our classifiers can be trained with large amounts of real traffic, rather than small synthetic data sets, and therefore have better performance.