Effective Program Debloating via Reinforcement Learning
- Title
- Effective Program Debloating via Reinforcement Learning
- Author
- 이우석
- Keywords
- Program debloating; reinforcement learning
- Issue Date
- 2018-10
- Publisher
- ACM
- Citation
- Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, Page. 380-394
- Abstract
- Prevalent software engineering practices such as code reuse and the "one-size-fits-all" methodology have contributed to significant and widespread increases in the size and complexity of software. The resulting software bloat has led to decreased performance and increased security vulnerabilities. We propose a system called Chisel to enable programmers to effectively customize and debloat programs. Chisel takes as input a program to be debloated and a high-level specification of its desired functionality. The output is a reduced version of the program that is correct with respect to the specification. Chisel significantly improves upon existing program reduction systems by using a novel reinforcement learning-based approach to accelerate the search for the reduced program and scale to large programs. Our evaluation on a suite of 10 widely used UNIX utility programs each comprising 13-90 KLOC of C source code demonstrates that Chisel is able to successfully remove all unwanted functionalities and reduce attack surfaces. Compared to two state-of-the-art program reducers C-Reduce and Perses, which time out on 6 programs and 2 programs espectively in 12 hours, Chisel runs up to 7.1x and 3.7x faster and finishes on all programs.
- URI
- https://dl.acm.org/citation.cfm?id=3243838https://repository.hanyang.ac.kr/handle/20.500.11754/105750
- ISBN
- 978-1-4503-5693-0
- ISSN
- 1543-7221
- DOI
- 10.1145/3243734.3243838
- Appears in Collections:
- COLLEGE OF COMPUTING[E](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
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