Demo: A symbolic N-variant system

Jun Xu, Pinyao Guo, Bo Chen, Robert F. Erbacher, Ping Chen, Peng Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This demo paper describes an approach to detect memory corruption attacks using artificial diversity. Our approach conducts offline symbolic execution of multiple variants of a system to identify paths which diverge in different vari- ants. In addition, we build an efficient input matcher to check whether an online input matches the constraints of a diverging path, to detect potential malicious input. By eval- uating the performance of a demo system built on Ghttpd, we find that per-input matching consumes only 70% to 96% of the real processing time in the master, which indicates a performance superiority for real world deployment.

Original languageEnglish (US)
Title of host publicationMTD 2016 - Proceedings of the 2016 ACM Workshop on Moving Target Defense, co-located with CCS 2016
PublisherAssociation for Computing Machinery, Inc
Pages65-68
Number of pages4
ISBN (Electronic)9781450345705
DOIs
StatePublished - Oct 24 2016
Event2016 ACM Workshop on Moving Target Defense, MTD 2016 - Vienna, Austria
Duration: Oct 24 2016 → …

Publication series

NameMTD 2016 - Proceedings of the 2016 ACM Workshop on Moving Target Defense, co-located with CCS 2016

Other

Other2016 ACM Workshop on Moving Target Defense, MTD 2016
CountryAustria
CityVienna
Period10/24/16 → …

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Computer Science Applications

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  • Cite this

    Xu, J., Guo, P., Chen, B., Erbacher, R. F., Chen, P., & Liu, P. (2016). Demo: A symbolic N-variant system. In MTD 2016 - Proceedings of the 2016 ACM Workshop on Moving Target Defense, co-located with CCS 2016 (pp. 65-68). (MTD 2016 - Proceedings of the 2016 ACM Workshop on Moving Target Defense, co-located with CCS 2016). Association for Computing Machinery, Inc. https://doi.org/10.1145/2995272.2995284