Title: Malware intelligence: beyond malware analysis

Authors: Ekta Gandotra; Divya Bansal; Sanjeev Sofat

Addresses: Department of Computer Science and Engineering, PEC University of Technology, Chandigarh, India ' Department of Computer Science and Engineering, PEC University of Technology, Chandigarh, India ' Department of Computer Science and Engineering, PEC University of Technology, Chandigarh, India

Abstract: A number of malware samples are available online but a little research has attempted to thoroughly analyse these for obtaining insights or intelligence about their behavioural trends, which can further be used to issue early warnings about future threats. In this paper, we have performed an in-depth analysis of about 0.1 million historical malware specimens in a sandbox environment to generate their attributes and behaviour. Afterwards, the intelligent information is mined using statistical analysis to study their behavioural trends and capabilities. The information so obtained can help to gain insight into the future measures that malware authors can use to design their programs. The paper also highlights the challenges evolving out of these trends which provide the future research directions to malware analysts and security researchers. Furthermore, this type of analysis facilitates research community in selecting the parameters/factors for building faster and improved techniques for detecting unknown malware.

Keywords: malware analysis; statistical analysis; security intelligence; behavioural trends; prediction.

DOI: 10.1504/IJAIP.2019.099945

International Journal of Advanced Intelligence Paradigms, 2019 Vol.13 No.1/2, pp.80 - 100

Received: 24 Aug 2016
Accepted: 14 Oct 2016
Published online: 29 May 2019 *

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