Department of Journalism and Communication, Anhui Vocational College of Press and Publishing, Hefei, 230601, Anhui, China
In recent years, deep learning has completely changed many machine learning tasks, and the data in these tasks is usually expressed in Euclidean space. However, as more and more applications need to use non-Euclidean data, vulnerability mining is becoming more and more important. With the successful development of neural networks, many machine learning tasks, such as object detection, image classification, and speech recognition, once relied heavily on manual feature engineering to extract features, and can now be completed with various end-to-end deep learning models, such as Convolutional neural network, long and short-term memory networketc.Vulnerability mining is an important way to prevent and control system vulnerabilities. Traditional methods of vulnerability mining can no longer meet people's needs. In order to enable the vulnerability mining application to meet people's needs, we established a related source code vulnerability mining model based on graph neural networks. By investigating relevant literature, conducting interviews with professionals, etc., collected data from databases such as HowNet, Wanfang Database, SSCI, etc., and built a model of source code vulnerability mining based on graph neural networks using parallel algorithms. Through simulation, we found that the method of mining source code vulnerabilities based on graph neural networks is becoming more and more accepted by people, and the increase in 2016 reached 0.16. Moreover, the efficiency of source code vulnerability mining based on graph neural network is much higher than other vulnerability mining methods, and the mining speed is more than 20% ahead of other mining methods. This shows that source code vulnerability mining based on graph neural network can play an important role in preventing system vulnerabilities.
Graph Neural Network, Machine Learning, Source Code, Research Method
Guo Li. Source Code Vulnerability Mining Method Based on Graph Neural Network. International Journal of Frontiers in Engineering Technology (2022), Vol. 4, Issue 4: 21-32. https://doi.org/10.25236/IJFET.2022.040404.
 Deng Qigui, Wei Bingui. Research on Vulnerability Mining Technology of Industrial Control System Based on Stain Analysis. Popular Science and Technology, 2019, 021(004):5-7,4.
 Wenping Li, Yu Liu, Wei Qiao. An Improved Vulnerability Assessment Model for Floor Water Bursting fr. Mine Water and the Environment, 2017, 37(1):1-9.
 Ghaffarian S M, Shahriari H R. Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques. ACM Computing Surveys (CSUR), 2017, 50(4):1-36.
 Tiwari A K , Singh P K , De Maio M . Evaluation of aquifer vulnerability in a coal mining of India by using GIS-based DRASTIC model. Arabian Journal of Geosciences, 2016, 000(6):1-15.
 Yang Y , Ren X , Zhang S , et al. Incorporating ecological vulnerability assessment into rehabilitation planning for a post-mining area. Environmental Earth Sciences, 2017, 76(6):245.
 Li J , Chen J , Huang M , et al. An Integration Testing Framework and Evaluation Metric for Vulnerability Mining Methods. Wireless Communication over ZigBee for Automotive Inclination Measurement. China Communications, 2018, 15(002):190-208.
 Wang C , Ren T , Li Q , et al. Network computer security hidden dangers and vulnerability mining technology. IOP Conference Series: Materials Science and Engineering, 2020, 750(1):121-155.
 Lai Y , Gao H , Liu J . Vulnerability Mining Method for the Modbus TCP Using an Anti-Sample Fuzzer. Sensors, 2020, 20(7):20-40.
 Ren T , Wang X , Li Q , et al. Vulnerability Mining Technology Based on Genetic Algorithm and Model Constraint. IOP Conference Series: Materials ence and Engineering, 2020, 750(1):122-128 .
 PawlakR ,Monperrus M , Petitprez N , et al. Spoon: A Library for Implementing Analyses and Transformations of Java Source Code. Software Practice and Experience, 2016, 46(9):1155-1179.
 Vamsi K G . Prediction of Source Code Quality Using Cyclomatic Complexity and Machine Learning. International Journal of Advanced Trends in Computer Science and Engineering, 2020, 9(4):4409-4413.
 Lee G , Yu J , Kim I , et al. Implementation of Software Source Code Obfuscation Tool for Weapon System Anti-Tampering. Journal of KIISE, 2019, 46(5):448-456.
 ManahiM, Sulaiman S, Bakar N S A A , et al. Source Code Plagarism Detection Approaches: A Systematic Literature Review. Journal of Advanced Research in Dynamical and Control Systems, 2020, 12(4-Special Issue):1575-1587.
 Jang Y S . Source Code Instrumentation Technique for Buffer Overflow Vulnerability Detection. The Journal of Korean Institute of Information Technology, 2019, 17(9):133-144.
 Wang W , Li G , Shen S , et al. Modular Tree Network for Source Code Representation Learning. ACM Transactions on Software Engineering and Methodology, 2020, 29(4):1-23.
 FranclintonR, Karnalim O . A Language-Independent Library for Observing Source Code Plagiarism. Journal of Information Systems Engineering and Business Intelligence, 2019, 5(2):110-119.
 Li Hang, Dong Wei, Zhu Guangyu. Research on Industrial Control Protocol Vulnerability Mining Technology Based on Fuzzing Test. Application of Electronic Technology, 2016, 42(7):79-82.
 Sha Letian, Xiao Fu, Yang Hongke, et al. IaaS layer vulnerability mining method based on adaptive fuzzing. Journal of Software, 2018, v.29(05):1303-1317.
 Sha Letian, Xiao Fu, Yang Hongke, et al. IaaS layer vulnerability mining method based on adaptive fuzzing. Journal of Software, 2018, 029(005):1303-1317.
 Zhou Min, Zhou Anmin, Liu Liang, et al. Mining denial of service vulnerability in Android applications automatically. Computer Applications, 2017, 037(011):3288-3293,3329.
 Liu Jinhui, Ge Lina, Zhang Jing, et al. Research on XSS Vulnerability Mining Technology Based on Fuzzy Testing. Network New Media Technology, 2016, v.5;No.25(01):13-20.
 QiuZhiqing, HuanFei. Vulnerability mining and detection tool based on web crawler and Fuzzing. Microcomputer Applications, 2016, v.32;No.275(03):73-76.
 Li Jiali, Chen Yongle, Li Zhi, et al. RTSP protocol vulnerability mining based on protocol state diagram traversal. Computer Science, 2018, 45(09):178-183.
 Fu Menglin, Wu Lifa, Hong Zheng, et al. Research on mining technology of smart contract security vulnerabilities. Journal of Computer Applications, 2019, 039(007):1959-1966.