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  • 1.
    Sokol, Maciej
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Ernstsson, Joakim
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Dynamic Heuristic Analysis Tool for Detection of Unknown Malware2016Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Context: In today's society virus makers have a large set of obfuscation tools to avoid classic signature detection used by antivirus software. Therefore there is a need to identify new and obfuscated viruses in a better way. One option is to look at the behaviour of a program by executing the program in a virtual environment to determine if it is malicious or benign. This approach is called dynamic heuristic analysis.

    Objectives: In this study a new heuristic dynamic analysis tool for detecting unknown malware is proposed. The proposed implementation is evaluated against state-of-the-art in terms of accuracy.

    Methods: The proposed implementation uses Cuckoo sandbox to collect the behavior of a software and a decision tree to classify the software as either malicious or benign. In addition, the implementation contains several custom programs to handle the interaction between the components.

    Results: The experiment evaluating the implementation shows that an accuracy of 90% has been reached which is higher than 2 out of 3 state-of-the-art software.

    Conclusions: We conclude that an implementation using Cuckoo and decision tree works well for classifying malware and that the proposed implementation has a high accuracy that could be increased in the future by including more samples in the training set.

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