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- W4285176031 abstract "Detecting software vulnerabilities are critical for limiting the damage caused by hostile exploits and program failures. This frequently necessitates the accurate identification of susceptible execution routes. However, as software complexity has increased, it has become notoriously difficult to find such susceptible paths by exploring the entire program execution space. Therefore, concolic testing is used in this paper as one of the ways to deal with this problem. Here, the observations and discoveries are collected from experimenting and implementing concolic testing. First, several trained classifier models like random forest, support vector machine, stochastic gradient descent, and AdaBoost are tested against a test dataset created by randomly selecting 30% of the data from the original dataset. Then, multiple classifier models help predict whether a program is faulty or benign. After testing out several classifier models, an ensemble is done on the top 3 highest accuracy classifiers. Overall, 87% accuracy is achieved with an F1-score of 85.1%. This result indicates that 87% of the program’s labels are accurately detected by our proposed model while higher F1-score represents the proposed model’s balanced detection." @default.
- W4285176031 created "2022-07-14" @default.
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- W4285176031 date "2022-01-01" @default.
- W4285176031 modified "2023-09-26" @default.
- W4285176031 title "Concolic-Based Software Vulnerability Prediction Using Ensemble Learning" @default.
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- W4285176031 doi "https://doi.org/10.1007/978-981-16-8739-6_21" @default.
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