Vulnerability Analysis of Instructions for SDC-Causing Error Detection

• Main Authors: Jingjing Gu, Weining Zheng, Yi Zhuang, Qianwen Zhang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Error Detection; Feature Extraction; Silent Data Corruption; Vulnerability Analysis
• Online Access: https://ieeexplore.ieee.org/document/8887166/

Due to the centralization of communication in the management of data generated by diverse Internet of Thing (IoT) devices, there is a lack of reliability when data is being transferred and stored. Among errors caused by various behaviors, Silent Data Corruption (SDC) error, owing to stealthy destruction without error prompt, is one of the most difficult data consistency problems in the storage system, whether it is a traditional multi-control, distributed storage, or public cloud storage. Nowadays, for SDC error detection, extracting instruction features to analyze vulnerabilities of programs or instructions has still not been fully explored. Literature generally just count the number of possible features, without mining the inter-characteristic of the instruction and correlation between them. Thus, we propose a method of SDC-causing Error Detection based on Support Vector Regression (SED-SVR) for fully exploiting the correlation between data features. Specifically, firstly, we extract instruction features based on the SDC vulnerability of program instructions by analyzing results of fault injections. Secondly, we establish the instruction SDC vulnerability prediction model based on SVR and propose our SED-SVR model. Thirdly, according to the predicted values of SDC vulnerability, we develop some solutions for faults tolerance of target programs by different granularity of instruction redundancy. The experimental results show that our SED-SVR has higher fault detection rate with lower performance overhead.