RENN: Efficient reverse execution with neural-network-assisted alias analysis

Dongliang Mu, Wenbo Guo, Alejandro Cuevas, Yueqi Chen, Jinxuan Gai, Xinyu Xing, Bing Mao, Chengyu Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reverse execution and coredump analysis have long been used to diagnose the root cause of software crashes. Each of these techniques, however, face inherent challenges, such as insufficient capability when handling memory aliases. Recent works have used hypothesis testing to address this drawback, albeit with high computational complexity, making them impractical for real world applications. To address this issue, we propose a new deep neural architecture, which could significantly improve memory alias resolution. At the high level, our approach employs a recurrent neural network (RNN) to learn the binary code pattern pertaining to memory accesses. It then infers the memory region accessed by memory references. Since memory references to different regions naturally indicate a non-alias relationship, our neural architecture can greatly reduce the burden of doing hypothesis testing to track down non-alias relation in binary code. Different from previous researches that have utilized deep learning for other binary analysis tasks, the neural network proposed in this work is fundamentally novel. Instead of simply using off-the-shelf neural networks, we designed a new recurrent neural architecture that could capture the data dependency between machine code segments. To demonstrate the utility of our deep neural architecture, we implement it as RENN, a neural network-assisted reverse execution system. We utilize this tool to analyze software crashes corresponding to 40 memory corruption vulnerabilities from the real world. Our experiments show that RENN can significantly improve the efficiency of locating the root cause for the crashes. Compared to a state-of-the-art technique, RENN has 36.25% faster execution time on average, detects an average of 21.35% more non-alias pairs, and successfully identified the root cause of 12.5% more cases.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages924-935
Number of pages12
ISBN (Electronic)9781728125084
DOIs
Publication statusPublished - Nov 2019
Event34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 - San Diego, United States
Duration: Nov 10 2019Nov 15 2019

Publication series

NameProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019

Conference

Conference34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
CountryUnited States
CitySan Diego
Period11/10/1911/15/19

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software
  • Control and Optimization

Cite this

Mu, D., Guo, W., Cuevas, A., Chen, Y., Gai, J., Xing, X., ... Song, C. (2019). RENN: Efficient reverse execution with neural-network-assisted alias analysis. In Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 (pp. 924-935). [8952186] (Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASE.2019.00090