Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2018
Abstract
Stack Smashing Protection (SSP) is a simple and highly efficient technique widely used in practice as the front line defense against stack buffer overflow attacks. Unfortunately, SSP is known to be vulnerable to the so-called byte-by-byte attack. Although several remedy schemes are proposed in the recent literature, their security is achieved at the price of practicality, because their complex logics ruin SSP's simplicity and high-efficiency. In this paper, we present an elegant solution named as Polymorphic SSP (P-SSP) that attains the same security without sacrificing SSP's strengths. We also propose three extensions of the basic scheme for better compatibility, stronger security, and local variable protection, respectively. We have implemented both a compiler plugin and a binary instrumentation tool for deploying P-SSP. Their respective runtime overheads are only 0.24% and 1.01%. We have also experimented with our extensions and compared their pros and cons with the basic scheme.
Keywords
Brute force attack, Canary, Stack buffer overflow
Discipline
Information Security
Research Areas
Cybersecurity
Publication
48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks DSN 2018: Luxembourg City, 25-28 June: Proceedings
First Page
243
Last Page
254
ISBN
9781538655955
Identifier
10.1109/DSN.2018.00035
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
WANG, Zhilong; DING, Xuhua; PANG, Chengbin; GUO, Jian; ZHU, Jun; and MAO, Bing.
To detect stack buffer overflow with polymorphic canaries. (2018). 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks DSN 2018: Luxembourg City, 25-28 June: Proceedings. 243-254.
Available at: https://ink.library.smu.edu.sg/sis_research/4101
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/DSN.2018.00035