  Malware evolves over time and antivirus must adapt to such evolution. Hence,
it is critical to detect those points in time where malware has evolved so that
appropriate countermeasures can be undertaken. In this research, we perform a
variety of experiments on a significant number of malware families to determine
when malware evolution is likely to have occurred. All of the evolution
detection techniques that we consider are based on machine learning and can be
fully automated -- in particular, no reverse engineering or other
labor-intensive manual analysis is required. Specifically, we consider analysis
based on hidden Markov models (HMM) and the word embedding techniques HMM2Vec
and Word2Vec.
