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Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental
Achieving the quantitative risk assessment has long been an elusive problem in information security, where the subjective and qualitative assessments dominate. This paper discusses the appropriateness of statistical and quantitative methods for information security risk management. Through case studies, we discuss di erent types of risks in terms of quantitative risk assessment, grappling with how to obtain distributions of both probability and consequence for the risks. N.N. Taleb's concepts of the Black Swan and the Four Quadrants provides the foundation for our approach and classi cation. We apply these concepts to determine where it is appropriate to apply quantitative methods, and where we should exert caution in our predictions. Our primary contribution is a treatise on di erent types of risk calculations, and a classi cation of information security threats within the Four Quadrants.
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