@inproceedings{f6bc1981355d4bbcac3d02e8e2776a90,
title = "Demo: A symbolic N-variant system",
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.",
author = "Jun Xu and Pinyao Guo and Bo Chen and Erbacher, {Robert F.} and Ping Chen and Peng Liu",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 ACM Workshop on Moving Target Defense, MTD 2016 ; Conference date: 24-10-2016",
year = "2016",
month = oct,
day = "24",
doi = "10.1145/2995272.2995284",
language = "English (US)",
series = "MTD 2016 - Proceedings of the 2016 ACM Workshop on Moving Target Defense, co-located with CCS 2016",
publisher = "Association for Computing Machinery, Inc",
pages = "65--68",
booktitle = "MTD 2016 - Proceedings of the 2016 ACM Workshop on Moving Target Defense, co-located with CCS 2016",
}