Malware Analysis and Static Call Graph Generation with Radare2

Author:

Mester AttilaORCID,

Abstract

"A powerful feature used in automated malware analysis is the static call graph of the executable file. Elimination of sandbox environment, fast scan, function call patterns beyond instruction level information – all of these motivate the prevalence of the feature. Processing and storing the static call graph of malicious samples in a scaled manner facilitates the application of complex network analysis in malware research. IDA Pro is one of the leading disassembler tools in the industry and can generate the call graph via GenCallGdl and GenFuncGdl APIs – a tool which was used in our previous works. In this paper an alternative analysis method is presented using another disassembler tool, Radare2, an open-source Unixbased software, which is also frequently used in this domain. Radare2 has Python support (among other languages), via the r2pipe package, thus enabling full scalability on Linux-based servers using containerized solutions. This paper offers a detailed technical description on how to use Radare2 to generate the static call graph of a PE file and a thorough comparison with the output of IDA Pro, as well as a public dataset on which the experiments were carried out. 2010 Mathematics Subject Classification. 68P25, 68P30. 1998 CR Categories and Descriptors. D.4.6 [Security and Protection]: Subtopic – Invasive software. Key words and phrases. malware analysis, static call graph, radare2, IDA Pro."

Publisher

Babes-Bolyai University

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference32 articles.

1. "1. Andriesse, D., Chen, X., Van Der Veen, V., Slowinska, A., and Bos, H. An in-depth analysis of disassembly on full-scale x86/x64 binaries. In USENIX Security Symposium (2016), pp. 583-600.

2. 2. Bai, J., Shi, Q., and Mu, S. A malware and variant detection method using function call graph isomorphism. Security and Communication Networks 2019 (2019), 1-12.

3. 3. Cohen, I. Deobfuscating apt32 flow graphs with cutter and radare2. Tech. rep., 2019.

4. 4. Cunningham, E., Boydell, O., Doherty, C., Roques, B., and Le, Q. Using text classification methods to detect malware. In AICS (2019).

5. 5. Dahl, G. E., Stokes, J. W., Deng, L., and Yu, D. Large-scale malware classification using random projections and neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (2013), IEEE, pp. 3422-3426.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3