  Malware ascription is a relatively unexplored area, and it is rather
difficult to attribute malware and detect authorship. In this paper, we employ
various Static and Dynamic features of malicious executables to classify
malware based on their family. We leverage Cuckoo Sandbox and machine learning
to make progress in this research. Post analysis, classification is performed
using various deep learning and machine learning algorithms. Using the features
gathered from VirusTotal (static) and Cuckoo (dynamic) reports, we ran the
vectorized data against Multinomial Naive Bayes, Support Vector Machine, and
Bagging using Decision Trees as the base estimator. For each classifier, we
tuned the hyper-parameters using exhaustive search methods. Our reports can be
extremely useful in malware ascription.
