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Hardware Assisted Randomization of Data

  • Brian Belleville
  • Hyungon Moon
  • Jangseop Shin
  • Dongil Hwang
  • Joseph M. Nash
  • Seonhwa Jung
  • Yeoul Na
  • Stijn Volckaert
  • Per Larsen
  • Yunheung Paek
  • Michael Franz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11050)

Abstract

Data-oriented attacks are gaining traction thanks to advances in code-centric mitigation techniques for memory corruption vulnerabilities. Previous work on mitigating data-oriented attacks includes Data Space Randomization (DSR). DSR classifies program variables into a set of equivalence classes, and encrypts variables with a key randomly chosen for each equivalence class. This thwarts memory corruption attacks that introduce illegitimate data flows. However, existing implementations of DSR trade precision for better run-time performance, which leaves attackers sufficient leeway to mount attacks. In this paper, we show that high precision and good run-time performance are not mutually exclusive. We present HARD, a precise and efficient hardware-assisted implementation of DSR. HARD distinguishes a larger number of equivalence classes, and incurs lower run-time overhead than software-only DSR. Our implementation achieves run-time overheads of just 6.61% on average, while the software version with the same protection costs 40.96%.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Brian Belleville
    • 1
  • Hyungon Moon
    • 2
  • Jangseop Shin
    • 2
  • Dongil Hwang
    • 2
  • Joseph M. Nash
    • 1
  • Seonhwa Jung
    • 2
  • Yeoul Na
    • 1
  • Stijn Volckaert
    • 1
  • Per Larsen
    • 1
  • Yunheung Paek
    • 2
  • Michael Franz
    • 1
  1. 1.University of California, IrvineIrvineUSA
  2. 2.ECE and ISRCSeoul National UniversitySeoulSouth Korea

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