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Domain Generation Algorithms (DGAs) are used by adversaries to establishCommand and Control (C\&C) server communications during cyber attacks.Blacklists of known/identified C\&C domains are often used as one of thedefense mechanisms. However, since blacklists are static and generated bysignature-based approaches, they can neither keep up nor detectnever-seen-before malicious domain names. Due to this shortcoming of blacklistdomain checking, machine learning algorithms have been used to address theproblem to some extent. However, when training is performed with limiteddatasets, the algorithms are likely to fail in detecting new DGA variants. Tomitigate this weakness, we successfully applied a DGA-based malicious domainclassifier using the Long Short-Term Memory (LSTM) method with a novel featureengineering technique. Our model's performance shows a higher level of accuracycompared to a previously reported model from prior research. Additionally, wepropose a new method using adversarial machine learning to generatenever-before-seen malware-related domain families that can be used toillustrate the shortcomings of machine learning algorithms in this regard.Next, we augment the training dataset with new samples such that it makestraining of the machine learning models more effective in detectingnever-before-seen malicious domain name variants. Finally, to protectblacklists of malicious domain names from disclosure and tampering, we devisesecure data containers that store blacklists and guarantee their protectionagainst adversarial access and modifications.

Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning