Identify Silent Data Corruption Vulnerable Instructions Using SVM

Yang, Na; Wang, Yun

doi:10.1109/access.2019.2905842

Cited by 18 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method is compared with other methods to accomplish its algorithm verification, which further illustrates the effectiveness and scalability of the proposed method. The regression algorithms involved in the comparison include logistic regression [37], SVM [38][39], random forest [40][41], SGD and the proposed method. The constructed model is shown in Fig.…”

Section: F Methods Comparison and Verificationmentioning

confidence: 99%

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In traditional fault diagnosis methods in power systems, it is difficult to accurately classify and predict the types of faults. With the emergence of big data technology, the fault classification and prediction methods based on big data analysis and processing have been applied in power systems. To make the classification and prediction of the fault types more accurate, this paper proposes a hybrid data mining method for power system fault classification and prediction based on clustering, association rules and stochastic gradient descent. This method uses a three-layer data mining model: The first layer uses the K-means clustering algorithm to preprocess the original fault data source, and it proposes to use self-encoding to simplify the data form. The second layer effectively eliminates the data that have little impact on the prediction results by using association rules, and the highly correlated data are mined to become the regression training data. The third layer first uses the cross-validation method to obtain the optimal parameters of each fault model, and then, it uses stochastic gradient descent for data regression training to obtain a classification and prediction model for each fault type. Finally, a verification example shows that compared with a single data mining algorithm model, the proposed method is more comparative in terms of the data mining, and the established power system fault classification and prediction model has global optimality and higher prediction accuracy, which has a certain feasibility for real-time online power system fault classification and prediction. This method reduces the disturbances from low-impact or irrelevant data by mining the fault data three times, and it uses cross-validation to optimize the multiple regression parameters of the regression model to solve the problems of low accuracy, large errors and easily falling into a local optimum, given the conduct of fault classification and prediction.

show abstract

Section: F Methods Comparison and Verificationmentioning

confidence: 99%

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Xu et al proposed a biased injection framework called Critical Fault to remove benign faults by the instruction-level vulnerability analysis [20]. Yang N et al proposed a method called Program Vulnerable Instructions Identification (PVInsiden) to identify program vulnerable instructions that were likely to cause SDCs [9]. Wang C et al proposed a neural network detector which could find out the SDC errors [10] iterated many times in the program.…”

Section: Related Workmentioning

confidence: 99%

“…p i is the vulnerability of the i th sample which is calculated during the fault injection phase. Then, we can obtain the vulnerability function prediction basec on SVR and shown in equation (9).…”

Section: ) Instruction Sdc Vulnerability Prediction Modelmentioning

confidence: 99%

“…Recently, literature focus on analyze vulnerable parts of the program and selectively reinforce them, including SDC vulnerability analysis based on fault injection [6], based on program static analysis [7] and machine learning-based SDC vulnerability analysis [8]. The fault injection methods injected the needed instruction information of faults into the schedule sequentially, which were often costly [6], [9], [10]. Detection methods based on static analysis analyzed features of instructions and the SDC vulnerability of programs without fault injections, but the time cost increased exponentially as the size of the program raised [11], [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vulnerability Analysis of Instructions for SDC-Causing Error Detection

Zheng

Zhuang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…Crash and hang are explicit errors that cause programs to respectively stop execution and to run non-stop, and they can be easily captured by those explicit behaviours. Being benign means that an error is masked during the program execution and does not have an effect on the output of the program [4]. SDC means that an error does not incur explicit behaviours; however, an incorrect result is produced after the program finishes.…”

Section: Introductionmentioning

confidence: 99%

F_Radish: Enhancing Silent Data Corruption Detection for Aerospace-Based Computing

Yang

Wang

2020

Electronics

Self Cite

View full text Add to dashboard Cite

Radiation-induced soft errors degrade the reliability of aerospace-based computing. Silent data corruption (SDC) is the most dangerous and insidious type of soft error result. To detect SDC, program invariant assertions are used to harden programs. However, there exist redundant assertions in hardened programs, which impairs the detection efficiency. Benign errors are another type of soft error result. An assertion may detect benign errors, incurring unnecessary recovery overhead. The detection degree of an assertion represents the detection capability, and an assertion with a high detection degree can detect severe errors. To improve the detection efficiency and detection degree while reducing the benign detection ratio, F_Radish is proposed in the present work to screen redundant assertions in a novel way. At a program point, the detection degree and benign detection ratio are considered to evaluate the importance of the assertions in the program point. As a result, only the most important assertion remains in the program point. Moreover, the redundancy degree is considered to screen redundant assertions for neighbouring program points. Experimental results show that in comparison with the Radish approach, the detection efficiency of F_Radish is about two times greater. Moreover, F_Radish reduces the benign detection ratio and improves the detection degree. It can avoid more unnecessary recovery overheads and detect more serious SDC than can Radish.

show abstract

Identify Silent Data Corruption Vulnerable Instructions Using SVM

Cited by 18 publications

References 14 publications

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Vulnerability Analysis of Instructions for SDC-Causing Error Detection

F_Radish: Enhancing Silent Data Corruption Detection for Aerospace-Based Computing

Contact Info

Product

Resources

About