Apply Statistical models such ass Hidden Markov Model, and makes use of Multiple Sequence Alignment and Profiling-HMM in hopes of classifying malware families
This repository contains the set of tools and scripts used to write the following Term Paper present to the Commission of Graduation of the Institute of Computing
Classifying Malware Using Dynamic Analysis and Profile Hidden Markov Models
Malicious software, also know as malware, is a persistent threat to the security of informa- tion systems. The continuous development of new exploits, attack methods and malware samples creates a need for security analysts to stay up to date with current threats. To aid in this task, tools such as dynamic analysis systems are used to extract informa- tion from malware samples. However, the volume of information extracted by such tools may be too large for manual analysis or inspection. In this work, we use Profile Hidden Markov Models to create statistical models using supervised learning. The models are generated by applying Multiple Sequence Alignment algorithm to the sequence of actions executed by the malware sample inside a dynamic analysis environment. A total of 606 malware samples were used, which we split into 301 for training the models and 305 for testing these created models. Those classification models allow the labeling of unknown samples, as well as uncovering information regarding malware oper- ation. When tested, the models performed well, since they obtained high F-Scores. We were also able to identify similar behavior among different malware families by comparing the labeling results present in the confusion matrix. The results obtained indicate that further research in this area can provide promising insights for defense against malware attacks.
Malware Analysis, Malware Classification, Malware Behavior, Malicious Software, Dynamic Analysis, Hidden Markov Models, Profile Hidden Markov Models, Multiple Sequence Alignment, Supervised Learning, Machine Learning
MIT License
Copyright (c) [2015] [Alexandre Or Cansian Baruque]
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