Instruction vulnerability prediction method and system based on deep random forest

Abstract

The invention discloses an instruction vulnerability prediction method and system based on a deep random forest, and the method comprises the steps: extracting the instruction feature information, related to the instruction vulnerability, of each program instruction, and generating an instruction feature vector representing the instruction vulnerability; performing fault injection on the trainingprogram to obtain a vulnerability value of each program instruction; generating an instruction vulnerability sample data set in combination with the instruction feature vector and the instruction vulnerability value; performing sliding sampling on the instruction vulnerability sample data set through a sliding window to generate an extended sample data set; constructing and training an instructionvulnerability prediction model based on the deep random forest; and extracting an instruction feature vector of the to-be-predicted target program, and combining the instruction feature vector with the instruction vulnerability prediction model to realize instruction vulnerability prediction of the to-be-predicted target program. The system is used for realizing the method. The method is high inprediction accuracy, low in demand for a sample set and less in required manual adjustment work, and can be effectively applied to prediction of instruction vulnerability after a program is affected by an instantaneous fault.

Description

technical field [0001] The invention belongs to the field of software reinforcement and software reliability, in particular to a method and system for predicting instruction vulnerability based on deep random forest. Background technique [0002] With the rapid development of semiconductor manufacturing technology, the size of computer chips continues to shrink, resulting in a significant increase in their sensitivity to space radiation. In the space radiation environment, the high-tech level integrated circuit chip is one of the main reasons for the failure of the computer system due to the single event flip effect caused by the radiation of high-energy particles or electromagnetic interference. Single event upset (Single Event Upset, SEU) refers to the phenomenon that a certain bit of the memory value is affected and the logic state is reversed. This kind of phenomenon is usually called soft error. [0003] Soft errors are usually divided into the following categories: (1...

AI Technical Summary

Problems solved by technology

In recent years, researches have used methods such as support vector machines and neural networks to predict program fragile instructions, but such methods require a large number of prediction data sets and complex manual parameter adjustments in order to achieve high accuracy.

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