@inproceedings{b37cc48c168e4018937c15a7fb81c63b,
title = "Algorithm diversity for resilient systems",
abstract = "Diversity can significantly increase the resilience of systems, by reducing the prevalence of shared vulnerabilities and making vulnerabilities harder to exploit. Work on software diversity for security typically creates variants of a program using low-level code transformations. This paper is the first to study algorithm diversity for resilience. We first describe how a method based on high-level invariants and systematic incrementalization can be used to create algorithm variants. Executing multiple variants in parallel and comparing their outputs provides greater resilience than executing one variant. To prevent different parallel schedules from causing variants{\textquoteright} behaviors to diverge, we present a synchronized execution algorithm for DistAlgo, an extension of Python for high-level, precise, executable specifications of distributed algorithms. We propose static and dynamic metrics for measuring diversity. An experimental evaluation of algorithm diversity combined with implementation-level diversity for several sequential algorithms and distributed algorithms shows the benefits of algorithm diversity.",
author = "Stoller, \{Scott D.\} and Liu, \{Yanhong A.\}",
note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2019.; 33rd Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2019 ; Conference date: 15-07-2019 Through 17-07-2019",
year = "2019",
doi = "10.1007/978-3-030-22479-0\_19",
language = "English",
isbn = "9783030224783",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "359--378",
editor = "Foley, \{Simon N.\}",
booktitle = "Data and Applications Security and Privacy XXXIII - 33rd Annual IFIP WG 11.3 Conference, DBSec 2019, Proceedings",
}