skip to main content
10.1145/3548606.3563529acmconferencesArticle/Chapter ViewAbstractPublication PagesccsConference Proceedingsconference-collections
poster

Poster: MUSTARD - Adaptive Behavioral Analysis for Ransomware Detection

Published:07 November 2022Publication History

ABSTRACT

Behavioural analysis based on filesystem operations is one of the most promising approaches for the detection of ransomware. Nonetheless, tracking all the operations on all the files for all the processes can introduce a significant overhead on the monitored system. We present MUSTARD, a solution to dynamically adapt the degree of monitoring for each process based on their behaviour to achieve a reduction of monitoring resources for the benign processes.

References

  1. 2016. ShieldFS dataset. http://shieldfs.necst.it. (2016). Accessed July 2022.Google ScholarGoogle Scholar
  2. 2017. State of Malware Report. Technical Report. Malwarebytes. https://www. malwarebytes.com/pdf/white-papers/stateofmalware.pdf Accessed July 2022.Google ScholarGoogle Scholar
  3. Andrea Continella, Alessandro Guagnelli, Giovanni Zingaro, Giulio De Pasquale, Alessandro Barenghi, Stefano Zanero, and Federico Maggi. 2016. Shieldfs: a self-healing, ransomware-aware filesystem. In Proceedings of the 32nd annual conference on computer security applications. 336--347.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Amin Kharaz, Sajjad Arshad, Collin Mulliner, William Robertson, and Engin Kirda. 2016. UNVEIL: A Large-Scale, Automated Approach to Detecting Ransomware. In 25th USENIX security symposium (USENIX Security 16). 757--772.Google ScholarGoogle Scholar
  5. Kyungroul Lee, Sun-Young Lee, and Kangbin Yim. 2019. Machine learning based file entropy analysis for ransomware detection in backup systems. IEEE Access 7 (2019), 110205--110215.Google ScholarGoogle ScholarCross RefCross Ref
  6. Timothy McIntosh, ASM Kayes, Yi-Ping Phoebe Chen, Alex Ng, and PaulWatters. 2021. Ransomware mitigation in the modern era: A comprehensive review, research challenges, and future directions. ACM Computing Surveys (CSUR) 54, 9 (2021), 1--36.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Routa Moussaileb, Nora Cuppens, Jean-Louis Lanet, and Hélène Le Bouder. 2021. A survey on windows-based ransomware taxonomy and detection mechanisms. ACM Computing Surveys (CSUR) 54, 6 (2021), 1--36.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Harun Oz, Ahmet Aris, Albert Levi, and A Selcuk Uluagac. 2021. A survey on ransomware: Evolution, taxonomy, and defense solutions. ACM Computing Surveys (CSUR) (2021).Google ScholarGoogle Scholar
  9. Nolen Scaife, Henry Carter, Patrick Traynor, and Kevin RB Butler. 2016. Cryptolock (and drop it): stopping ransomware attacks on user data. In 2016 IEEE 36th international conference on distributed computing systems (ICDCS). IEEE, 303--312.Google ScholarGoogle ScholarCross RefCross Ref
  10. Kimberly Tam, Aristide Fattori, Salahuddin Khan, and Lorenzo Cavallaro. 2015. Copperdroid: Automatic reconstruction of android malware behaviors. In NDSS Symposium 2015. 1--15.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Poster: MUSTARD - Adaptive Behavioral Analysis for Ransomware Detection

    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 '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
      November 2022
      3598 pages
      ISBN:9781450394505
      DOI:10.1145/3548606

      Copyright © 2022 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: 7 November 2022

      Check for updates

      Qualifiers

      • poster

      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
    • Article Metrics

      • Downloads (Last 12 months)111
      • Downloads (Last 6 weeks)5

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader