DouBiGRU-A: Software defect detection algorithm based on attention mechanism and double BiGRU. Issue 111 (December 2021)

• Record Type: Journal Article
• Authors: Zhao, Jinxiong; Guo, Sensen; Mu, Dejun
• Is Part Of: Computers & security. Issue 111(2021)
• Publication Date: 2021-12
• Subjects: DouBiGRU-A -- Software defect detection -- Vulnerability identification -- Flawfinder, RATS; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2021.102459
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: Software defects such as errors, bugs, and failures lead to poor usability and low efficiency, severely degrading the user experience. Bugs in the code are among the key areas of software defects. The exploitability of such vulnerabilities can bring about a series of security problems, such as user information leakage and network attacks. Most traditional solutions in software vulnerability detection rely on practical knowledge and experience for manual labeling and classification. Manual methods can effectively detect vulnerabilities with a high degree of attention, but those with a low degree of attention have relatively high false negative and false positive rates. Solutions based on software defect code data sets are available, which use deep learning to train software vulnerability identification models, reducing the dependence on manual knowledge and experience, but the precision rate (P) of the models and the F1 score are generally low. In this paper, based on the NVD and SARD data sets, we propose a software defect detection algorithm DouBiGRU-A that combines bidirectional gated recurrent unit (BiGRU) and an attention mechanism. In the experimental simulation, comparison with the Li-Method, bilateral long short-term memory (BiLSTM), BiGRU, and BiLSTM&Attention shows that on the CWE-399 data set, the P and F1 scores of DouBiGRU-A are 0.7% and 0.80% higher than the Li-Method, respectively. Moreover, in the CWE-399 data set, the P and F1 scores of DouBiGRU-A are 28.2% and 43.45% higher than the average values for Flawfinder and RATS, respectively. On the CWE-119 data set, the F1 score of DouBiGRU-A is 2.73% higher than the Li-Method; the P and F1 scores of DouBiGRU-A are 63.07% and 53.98% higher than the average values of Flawfinder and RATS, respectively. On the combined CWE-119&CWE-399 data set, the P and F1 scores of DouBiGRU-A are 5.22% and 4.29% higher than Li-Method, respectively. The P and F1 scores of DouBiGRU-A are 59.72% and 46.59% higher than the average values of Flawfinder and RATS, respectively.