Name
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 variants. 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 evaluating 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2995272.2995284 | MTD@CCS |
Keywords | DocType | Citations |
Diversification,N-Variant,Symbolic execution | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Xu | 1 | 65 | 10.29 |
| Pinyao Guo | 2 | 24 | 4.66 |
| Bo Chen | 3 | 144 | 15.07 |
| Robert F. Erbacher | 4 | 202 | 27.65 |
| Ping Chen | 5 | 0 | 0.34 |
| Peng Liu | 6 | 1701 | 171.49 |