  Domain Generation Algorithms (DGAs) are used by adversaries to establish
Command and Control (C\&C) server communications during cyber attacks.
Blacklists of known/identified C\&C domains are often used as one of the
defense mechanisms. However, since blacklists are static and generated by
signature-based approaches, they can neither keep up nor detect
never-seen-before malicious domain names. Due to this shortcoming of blacklist
domain checking, machine learning algorithms have been used to address the
problem to some extent. However, when training is performed with limited
datasets, the algorithms are likely to fail in detecting new DGA variants. To
mitigate this weakness, we successfully applied a DGA-based malicious domain
classifier using the Long Short-Term Memory (LSTM) method with a novel feature
engineering technique. Our model's performance shows a higher level of accuracy
compared to a previously reported model from prior research. Additionally, we
propose a new method using adversarial machine learning to generate
never-before-seen malware-related domain families that can be used to
illustrate the shortcomings of machine learning algorithms in this regard.
Next, we augment the training dataset with new samples such that it makes
training of the machine learning models more effective in detecting
never-before-seen malicious domain name variants. Finally, to protect
blacklists of malicious domain names from disclosure and tampering, we devise
secure data containers that store blacklists and guarantee their protection
against adversarial access and modifications.
