NDLI: Software Profiling Options and Their Effects on Security Based Diversification

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Software Profiling Options and Their Effects on Security Based Diversification

Content Provider	ACM Digital Library
Author	Larsen, Per Murphy, Mark Brunthaler, Stefan Franz, Michael
Abstract	Imparting diversity to binaries by inserting garbage instructions is an effective defense against code-reuse attacks. Relocating and breaking up code gadgets removes an attacker's ability to craft attacks by merely studying the existing code on their own computer. Unfortunately, inserting garbage instructions also slows down program execution. The use of profiling enables optimizations that alleviate much of this overhead, while still maintaining the high level of security needed to deter attacks. These optimizations are performed by varying the probability for the insertion of a garbage instruction at any particular location in the binary. The hottest regions of code get the smallest amount of diversification, while the coldest regions get the most diversification. We show that static and dynamic profiling methods both reduce run-time overhead to under 2.5% while preventing over 95% of original gadgets from appearing in any diversified binary. We compare static and dynamic profiling and find that dynamic profiling has a slight performance advantage in a best-case scenario. But we also show that dynamic profiling results can suffer greatly from bad training input. Additionally, we find that static profiling creates smaller binary files than dynamic profiling, and that the two methods offer nearly identical security characteristics.
Starting Page	87
Ending Page	96
Page Count	10
File Format	PDF
ISBN	9781450331500
DOI	10.1145/2663474.2663485
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Code randomization Static profiling Automated software diversity Dynamic profiling
Content Type	Text
Resource Type	Article

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