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Modern malware families often rely on domain-generation algorithms (DGAs) todetermine rendezvous points to their command-and-control server. Traditionaldefence strategies (such as blacklisting domains or IP addresses) areinadequate against such techniques due to the large and continuously changinglist of domains produced by these algorithms. This paper demonstrates that amachine learning approach based on recurrent neural networks is able to detectdomain names generated by DGAs with high precision. The neural models areestimated on a large training set of domains generated by various malwares.Experimental results show that this data-driven approach can detectmalware-generated domain names with a F_1 score of 0.971. To put itdifferently, the model can automatically detect 93 % of malware-generateddomain names for a false positive rate of 1:100.

Automatic Detection of Malware-Generated Domains with Recurrent Neural Models