Effective program debloating via reinforcement learning

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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 respectively in 12 hours, Chisel runs up to 7.1x and 3.7x faster and finishes on all programs.
Publisher
Association for Computing Machinery
Issue Date
2018-10-15
Language
English
Citation

25th ACM Conference on Computer and Communications Security, CCS 2018, pp.380 - 394

DOI
10.1145/3243734.3243838
URI
http://hdl.handle.net/10203/277249
Appears in Collection
CS-Conference Papers(학술회의논문)
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