skip to main content
10.1145/3460120.3485355acmconferencesArticle/Chapter ViewAbstractPublication PagesccsConference Proceedingsconference-collections
demonstration

MANIAC: A Man-Machine Collaborative System for Classifying Malware Author Groups

Published:13 November 2021Publication History

ABSTRACT

In this demo, we show MANIAC, a MAN-machIne collaborative system for malware Author Classification. It is developed to fight a number of author groups who have been generating lots of new malwares by sharing source code within a group and exploiting evasive schemes such as polymorphism and metamorphism. Notably, MANIAC allows users to intervene in the model's classification of malware authors with high uncertainty. It also provides effective interfaces and visualizations with users to achieve maximum classification accuracy with minimum human labor.

Skip Supplemental Material Section

Supplemental Material

PP047D.mp4

mp4

49.6 MB

References

  1. D. Bilar. 2007. Opcodes as predictor for malware. International Journal of Electronic Security and Digital Forensics 1, 2 (2007), 156--168.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. D.-K. Chae et al. 2013. Software plagiarism detection: a graph-based approach. In ACM CIKM. 1577--1580.Google ScholarGoogle Scholar
  3. S. Chakkaravarthy, D. Sangeetha, and V. Vaidehi. 2019. A Survey on malware analysis and mitigation techniques. Computer Science Review 32 (2019), 1--23.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. G. Costantini, P. Ferrara, and A. Cortesi. 2011. Static analysis of string values. In International Conference on Formal Engineering Methods. 505--521.Google ScholarGoogle Scholar
  5. F. M. Dekking et al. 2005. A Modern Introduction to Probability and Statistics: Understanding why and how. Springer Science & Business Media.Google ScholarGoogle Scholar
  6. M. Egele et al. 2008. Asurvey on automated dynamic malware-analysis techniques and tools. ACM computing surveys (CSUR) 44, 2 (2008), 1--42.Google ScholarGoogle Scholar
  7. A. Grégio et al. 2011. Behavioral analysis of malicious code through network traffic and system call monitoring. 8059 (2011), 80590O.Google ScholarGoogle Scholar
  8. Jiawei Han, Jian Pei, and Micheline Kamber. 2011. Data mining: concepts and techniques. Elsevier.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Hong et al. 2019. Malware classification for identifying author groups: a graph-based approach. In ACM RACS. 169--174.Google ScholarGoogle Scholar
  10. B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. In ACM SIGKDD. 701--710.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. Plohmann et al. 2017. Malpedia: a collaborative effort to inventorize the malware landscape. Proceedings of the Botconf (2017).Google ScholarGoogle Scholar

Index Terms

  1. MANIAC: A Man-Machine Collaborative System for Classifying Malware Author Groups

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
        November 2021
        3558 pages
        ISBN:9781450384544
        DOI:10.1145/3460120

        Copyright © 2021 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 13 November 2021

        Check for updates

        Qualifiers

        • demonstration

        Acceptance Rates

        Overall Acceptance Rate1,261of6,999submissions,18%

        Upcoming Conference

        CCS '24
        ACM SIGSAC Conference on Computer and Communications Security
        October 14 - 18, 2024
        Salt Lake City , UT , USA

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader