标题：
Instruction SDC Vulnerability prediction using long short-term memory neural network
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所属单位：
计算机科学与技术学院/人工智能学院/软件学院
发表刊物：
Lect. Notes Comput. Sci.
摘要：
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. © 2018, Springer Nature Switzerland AG.
ISSN号：
0302-9743
是否译文：
否
发表时间：
2018-01-01
合写作者：
Liu, Yunfei,庄毅
通讯作者：
李静
发表时间：
2018-01-01
学位：工学博士学位
职称：副教授
所在单位：计算机科学与技术学院/人工智能学院/软件学院