Publication Type
Conference Proceeding Article
Publication Date
9-2014
Abstract
Concolic testing is widely regarded as the state-of-the-art technique in dynamic discovering and analyzing trigger-based behavior in software programs. It uses symbolic execution and an automatic theorem prover to generate new concrete test cases to maximize code coverage for scenarios like software verification and malware analysis. While malicious developers usually try their best to hide malicious executions, there are also circumstances in which legitimate reasons are presented for a program to conceal trigger-based conditions and the corresponding behavior, which leads to the demand of control flow obfuscation techniques. We propose a novel control flow obfuscation design based on the incomprehensibility of artificial neural networks to fight against reverse engineering tools including concolic testing. By training neural networks to simulate conditional behaviors of a program, we manage to precisely replace essential points of a program’s control flow with neural network computations. Evaluations show that since the complexity of extracting rules from trained neural networks easily goes beyond the capability of program analysis tools, it is infeasible to apply concolic testing on code obfuscated with our method. Our method also incorporates only basic integer operations and simple loops, thus can be hard to be distinguished from regular programs.
Keywords
Software obfuscation, malware analysis, reverse engineering, concolic testing, neural network
Discipline
Information Security
Research Areas
Cybersecurity
Publication
International Conference on Security and Privacy in Communication Networks: 10th International ICST Conference, SecureComm 2014, Beijing, China, September 24-26, 2014, Revised Selected Papers, Part I
Volume
152
First Page
287
Last Page
304
ISBN
9783319238289
Identifier
10.1007/978-3-319-23829-6_21
Publisher
Springer Verlag
City or Country
New York
Citation
Ma, Haoyu; Ma, Xinjie; Liu, Weijie; Huang, Zhipeng; GAO, Debin; and Jia, Chunfu.
Control Flow Obfuscation using Neural Network to Fight Concolic Testing. (2014). International Conference on Security and Privacy in Communication Networks: 10th International ICST Conference, SecureComm 2014, Beijing, China, September 24-26, 2014, Revised Selected Papers, Part I. 152, 287-304. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2260
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Additional URL
http://dx.doi.org/10.1007/978-3-319-23829-6_21