Version 1
: Received: 22 August 2022 / Approved: 26 August 2022 / Online: 26 August 2022 (03:50:01 CEST)
Version 2
: Received: 24 September 2022 / Approved: 26 September 2022 / Online: 26 September 2022 (04:24:48 CEST)
How to cite:
Musgrave, J.; Campan, A.; Messay-Kebede, T.; Kapp, D.; Ralescu, A. Empirical Network Structure of Malicious Programs. Preprints2022, 2022080440. https://doi.org/10.20944/preprints202208.0440.v1
Musgrave, J.; Campan, A.; Messay-Kebede, T.; Kapp, D.; Ralescu, A. Empirical Network Structure of Malicious Programs. Preprints 2022, 2022080440. https://doi.org/10.20944/preprints202208.0440.v1
Musgrave, J.; Campan, A.; Messay-Kebede, T.; Kapp, D.; Ralescu, A. Empirical Network Structure of Malicious Programs. Preprints2022, 2022080440. https://doi.org/10.20944/preprints202208.0440.v1
APA Style
Musgrave, J., Campan, A., Messay-Kebede, T., Kapp, D., & Ralescu, A. (2022). Empirical Network Structure of Malicious Programs. Preprints. https://doi.org/10.20944/preprints202208.0440.v1
Chicago/Turabian Style
Musgrave, J., David Kapp and Anca Ralescu. 2022 "Empirical Network Structure of Malicious Programs" Preprints. https://doi.org/10.20944/preprints202208.0440.v1
Abstract
A modern binary executable is made up of various networks. This study is an empirical 1
analysis of the networks composing malicious binaries from multiple samples and quantifies their 2
structural composition with network measurements. We demonstrate the presence of Scale-Free 3
properties for data dependency and control flow graphs, and show that data dependency graphs 4
have both Scale-Free and Small-World properties. We show that program data dependency graphs 5
have a degree correlation that is disassortative, and that control flow graphs have a neutral degree 6
assortativity. These network measurements provide a set of features for further classification tasks to 7
identify patterns of malicious programs.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
