NDLI: Analysis of malware behavior: Type classification using machine learning

Content Provider	IEEE Xplore Digital Library
Author	Pirscoveanu, Radu S. Hansen, Steven S. Larsen, Thor M. T. Stevanovic, Matija Pedersen, Jens Myrup Czech, Alexandre
Copyright Year	2015
Description	Author affiliation: Ecole Centrale d'Electronique, Paris, France (Czech, Alexandre) \|\| Aalborg University, Denmark (Pirscoveanu, Radu S.; Hansen, Steven S.; Larsen, Thor M. T.; Stevanovic, Matija; Pedersen, Jens Myrup) Malicious software has become a major threat to modern society, not only due to the increased complexity of the malware itself but also due to the exponential increase of new malware each day. This study tackles the problem of analyzing and classifying a high amount of malware in a scalable and automatized manner. We have developed a distributed malware testing environment by extending Cuckoo Sandbox that was used to test an extensive number of malware samples and trace their behavioral data. The extracted data was used for the development of a novel type classification approach based on supervised machine learning. The proposed classification approach employs a novel combination of features that achieves a high classification rate with a weighted average AUC value of 0.98 using Random Forests classifier. The approach has been extensively tested on a total of 42,000 malware samples. Based on the above results it is believed that the developed system can be used to pre-filter novel from known malware in a future malware analysis system.
Starting Page	1
Ending Page	7
File Size	587991
Page Count	7
File Format	PDF
ISBN	9780993233807
DOI	10.1109/CyberSA.2015.7166128
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-08
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Centre for Multidisciplinary Research, Innovation and Collaboration (C-MRiC.ORG)
Subject Keyword	Supervised machine learning Feature selection Scalability Dynamic analysis API call Random Forests Data mining Type-classification Training Cuckoo sandbox Vegetation Feature extraction Malware Trojan horses Testing
Content Type	Text
Resource Type	Article

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