Labels Predicted by AI
Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.
Abstract
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.