MTD techniques for memory protection against zero-day attacks

Ping chen, Zhisheng Hu, Jun Xu, Minghui Zhu, Rob Erbacher, Sushil Jajodia, Peng Liu

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

During the past 25 years, the arms race between attacks exploiting memory corruption and memory protection techniques has drawn tremendous attention. This book chapter seeks to give an in-depth review of the newest research progress made on applying the MTD methodology to protect memory corruption exploits. The new research progress also represents the current phase of the arms race in the MTD perspective. In particular, on one hand, at the frontier of defending against control-hijacking attacks, we will give an in-depth review on the shift of defense strategy from static ASLR to dynamic ASLR. On the other hand, at the frontier of defending against data-oriented attacks, we will give an in-depth review on the shift of defense strategy from static DSLR to dynamic DSLR.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages129-155
Number of pages27
DOIs
StatePublished - Jan 1 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11830 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Attack
Data storage equipment
Zero
Methodology
Review
Strategy

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

chen, P., Hu, Z., Xu, J., Zhu, M., Erbacher, R., Jajodia, S., & Liu, P. (2019). MTD techniques for memory protection against zero-day attacks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 129-155). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11830 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-30719-6_7
chen, Ping ; Hu, Zhisheng ; Xu, Jun ; Zhu, Minghui ; Erbacher, Rob ; Jajodia, Sushil ; Liu, Peng. / MTD techniques for memory protection against zero-day attacks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, 2019. pp. 129-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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chen, P, Hu, Z, Xu, J, Zhu, M, Erbacher, R, Jajodia, S & Liu, P 2019, MTD techniques for memory protection against zero-day attacks. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11830 LNCS, Springer Verlag, pp. 129-155. https://doi.org/10.1007/978-3-030-30719-6_7

MTD techniques for memory protection against zero-day attacks. / chen, Ping; Hu, Zhisheng; Xu, Jun; Zhu, Minghui; Erbacher, Rob; Jajodia, Sushil; Liu, Peng.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, 2019. p. 129-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11830 LNCS).

Research output: Chapter in Book/Report/Conference proceedingChapter

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chen P, Hu Z, Xu J, Zhu M, Erbacher R, Jajodia S et al. MTD techniques for memory protection against zero-day attacks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2019. p. 129-155. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-30719-6_7