Advancing Digital Forensics

Franke, Katrin; Hjelmås, Erik; Wolthusen, Stephen D.

doi:10.1007/978-3-642-39377-8_34

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Attackers have leveraged malware to target personal computers [22], mobile devices [61], cloud storage systems [13], Supervisory Control and Data Acquisition Systems (SCADA) [12], Internet of Things (IoT) network [81] and even big data platforms [67].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

Shalaginov

Banin

Dehghantanha

et al. 2018

Advances in Information Security

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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

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