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Secure and practical defense against code-injection attacks using software dynamic translation
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Source ACM/Usenix International Conference On Virtual Execution Environments archive
Proceedings of the 2nd international conference on Virtual execution environments table of contents
Ottawa, Ontario, Canada
SESSION: Security and eliability table of contents
Pages: 2 - 12  
Year of Publication: 2006
ISBN:1-59593-332-6
Authors
Wei Hu  University of Virginia
Jason Hiser  University of Virginia
Dan Williams  University of Virginia
Adrian Filipi  University of Virginia
Jack W. Davidson  University of Virginia
David Evans  University of Virginia
John C. Knight  University of Virginia
Anh Nguyen-Tuong  University of Virginia
Jonathan Rowanhill  University of Virginia
Sponsors
ACM: Association for Computing Machinery
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
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ABSTRACT

One of the most common forms of security attacks involves exploiting a vulnerability to inject malicious code into an executing application and then cause the injected code to be executed. A theoretically strong approach to defending against any type of code-injection attack is to create and use a process-specific instruction set that is created by a randomization algorithm. Code injected by an attacker who does not know the randomization key will be invalid for the randomized processor effectively thwarting the attack. This paper describes a secure and efficient implementation of instruction-set randomization (ISR) using software dynamic translation. The paper makes three contributions beyond previous work on ISR. First, we describe an implementation that uses a strong cipher algorithm--the Advanced Encryption Standard (AES), to perform randomization. AES is generally believed to be impervious to known attack methodologies. Second, we demonstrate that ISR using AES can be implemented practically and efficiently (considering both execution time and code size overheads) without requiring special hardware support. The third contribution is that our approach detects malicious code before it is executed. Previous approaches relied on probabilistic arguments that execution of non-randomized foreign code would eventually cause a fault or runtime exception.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Wei Hu: colleagues
Jason Hiser: colleagues
Dan Williams: colleagues
Adrian Filipi: colleagues
Jack W. Davidson: colleagues
David Evans: colleague listing is not available.
John C. Knight: colleagues
Anh Nguyen-Tuong: colleagues
Jonathan Rowanhill: colleagues