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Instruction SDC Vulnerability Prediction Using Long Short-Term Memory Neural Network

  • Yunfei Liu
  • Jing LiEmail author
  • Yi Zhuang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

Silent Data Corruption (SDC) is one of the serious issues in soft errors and it is difficult to detect because it can cause erroneous results without any indication. In order to solve this problem, a new SDC vulnerability prediction method based on deep learning model is proposed. Our method predicts the SDC vulnerability of each instruction in the program based on the inherent and dependent features of each instruction in the Lower Level Virtual Machine (LLVM) intermediate. Firstly, the features are extracted from benchmarks by LLVM passes and feature selection is performed. Then, LLVM Based Fault Injection Tool (LLFI) is used to get SDC vulnerability labels to obtain the SDC prediction data set. Long Short-Term Memory (LSTM) neural network is applied to classification of SDC vulnerability. Finally, compared with the model based on SVM and Decision Tree, the experiment results show that the average accuracy of LSTM in classification of SDC vulnerability is 11.73% higher than SVM, and 10.74% higher than Decision Tree.

Keywords

LSTM Silent data corruption Fault injection Prediction 

Notes

Acknowledgement

This paper is supported by the Fundamental Research Funds for the Central Universities (NS 2015092).

References

  1. 1.
    Walter, J.P., Zick, K.M., French, M.: A practical characterization of a NASA spacecube application through fault emulation and laser testing. In: IEEE International Conference on Dependable Systems and Networks, pp. 1–8 (2013)Google Scholar
  2. 2.
    Reis, G.A., Chang, J., Vachharajani, N., Rangan, R.: SWIFT: software implemented fault tolerance. In: International Symposium on Code Generation and Optimization, pp. 243–254 (2005)Google Scholar
  3. 3.
    Hari, S.K.S., Adve, S.V., Naeimi, H.: Low-cost program-level detectors for reducing silent data corruptions. In: IEEE/IFIP International Conference on Dependable Systems and Networks, pp. 1–12 (2012)Google Scholar
  4. 4.
    Lu, Q., Pattabiraman, K., Gupta, M.S.: SDCTune: a model for predicting the SDC proneness of an application for configurable protection. In: International Conference on Compilers, Architecture and Synthesis for Embedded Systems, pp. 1–10 (2015)Google Scholar
  5. 5.
    Farnsworth, C., Clark, L.T., Gogulamudi, A.R.: A soft-error mitigated microprocessor with software controlled error reporting and recovery. IEEE Trans. Nuclear Sci. 63(4), 2241–2249 (2016)CrossRefGoogle Scholar
  6. 6.
    Elnozahy, E.N., Plank, J.S.: Checkpointing for peta-scale systems: a look into the future of practical rollback-recovery. IEEE Trans. Dependable Secur. Comput. 1(2), 97–108 (2004)CrossRefGoogle Scholar
  7. 7.
    Bernick, D., Bruckert, B., Vigna, P.D.: NonStop® advanced architecture. In: International Conference on Dependable Systems and Networks, pp. 12–21 (2005)Google Scholar
  8. 8.
    Chang, J., Reis, G.A., August, D.I.: Automatic instruction-level software-only recovery. IEEE Micro 27(1), 36–47 (2007)CrossRefGoogle Scholar
  9. 9.
    Thomas, A., Pattabiraman, K.: Error detector placement for soft computation. In: 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, pp. 1–12 (2013)Google Scholar
  10. 10.
    Laguna, I., Schulz, M., Richards, D.F.: IPAS: intelligent protection against silent output corruption in scientific applications. In: IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pp. 227–238 (2016)Google Scholar
  11. 11.
    Zhao, J., Qu, H., Zhao, J.: Towards traffic matrix prediction with LSTM recurrent neural networks. Electron. Lett. 54(9), 566–568 (2018)CrossRefGoogle Scholar
  12. 12.
    Chen, W.T., et al.: EEG-based motion intention recognition via multi-task RNNs. In: Proceedings of the 2018 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, pp. 279–287 (2018)CrossRefGoogle Scholar
  13. 13.
    Yue, L., Chen, W.T., Li, X., Zuo, W.L., Yin, M.H.: A survey of sentiment analysis in social media. Knowl. Inf. Syst. 1–47 (2018)Google Scholar
  14. 14.
    Greff, K., Srivastava, R.K., Koutník, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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