Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220600172-6.doi: 10.11896/jsjkx.220600172
• Information Security • Previous Articles Next Articles
WANG Qingyu, WANG Hairui, ZHU Guifu, MENG Shunjian
CLC Number:
| [1]Top 10 Web Application Security Risks[EB/OL].https://owasp.org/www-project-top-ten. [2]OWASP TOP 10 from 2003 to 2021 Releases[EB/OL].https://github.com/OWASP/Top10. [3]WANG F.Research and implementation of SQL injection detection technology based on d-eep learning[D].Beijing:Beijing University of Posts and Telecommunications,2020. [4]GOULD C,SU Z,DEVANBU P.Static checking of dynamically generated queries in database applications[C]//26th International Conference on Software Engineering.IEEE,2004:645-654. [5]LIVSHITS V B,LAM M S.Finding Security Vulnerabilities in Java Applications with Static Analysis[C]//Proceedings of the 14th Conference on USENIX Security Symposium.2005:18. [6]SHIN Y.Improving the identification of actual input manipula-tion vulnerabilities[C]//14th ACM SIGSOFT Symposium on Foundations of Software Engineering ACM.2006. [7]DAS D,SHARMA U,BHATTACHARYYAD K.An Approach to Detection of SQL Injection Vulnerabilities Based on Dynamic Query Matching[J].International Journal of Computer Applications,2010,1(25):39-45. [8]HALFOND W G J,ORSO A.AMNESIA:analysis and monito-ring for neutralizing SQL inj-ection attacks[C]//Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering.2005:174-183. [9]XIAO Z,ZHOU Z,YANG W,et al.An approach for SQL injection detection based on behavior and response analysis[C]//2017 IEEE 9th International Conference on Communication Software and Networks(ICCSN).IEEE,2017:1437-1442. [10]APPIAH B,OPOKU-MENSAH E,QIN Z.SQL injection attack detection using fingerprints and pattern matching technique[C]//2017 8th IEEE International Conference on Software Engineering and Service Science(ICSESS).IEEE,2017:583-587. [11]WASSERMANN G,GOULD C,SU Z D,et al.Static Checking of Dynamically Generated Queries in Database Applications[J].ACM Transactions on Software Engineering and Methodology,2007,16(4):14.1-14.27. [12]ISHITAKI T,OBUKATA R,ODAT,et al.Application of deep recurrent neural networks for prediction of user behavior in tor networks[C]//2017 31st International Conference on Advanced Information Networking and Applications Workshops(WAINA).IEEE,2017:238-243. [13]ZHANG X Y.Research on patriotism in class-ical poetry based on textcnn[D].Shanghai:Shanghai Normal University,2020. [14]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444. [15]CHEN Y.Convolutional neural networks for sentence classification[D].Waterloo:University of Waterloo,2015. [16]XIE Q,DAI Z,HOVY E,et al.Unsupervised data augmentation for consistency training[J].Advances in Neural Information Processing Systems,2020,33:6256-6268. [17]ZHANG B,WANG Y,HOU W,et al.Flexmatch:Boostingsemi-supervised learning with curriculum pseudo labeling[J].Advances in Neural Information Processing Systems, 2021,34:18408-18419. [18]SQL injection dataset[EB/OL].[https://github.com/client9/libinjection. [19]JOSHI A,GEETHA V.SQL Injection detection using machine learning[C]//2014 International Conference on Control,Instrumentation,Communication and Computational Technologies(ICCICCT).IEEE,2014:1111-1115. [20]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estima-tion of word representations in vector space[J].arXiv:1301.3781,2013. [21]LI C.Research on SQL injection detection technology based on Naive Bayes and LST-M recurrent neural network[D].Changsha:Hunan University,2018. [22]CAO X B.Research on SQL injection detect-ion based on deep learning[D].Nanning:Guangxi University,2020. |
| [1] | LI Hui, LI Wengen, GUAN Jihong. Dually Encoded Semi-supervised Anomaly Detection [J]. Computer Science, 2023, 50(7): 53-59. |
| [2] | GU Yuhang, HAO Jie, CHEN Bing. Semi-supervised Semantic Segmentation for High-resolution Remote Sensing Images Based on DataFusion [J]. Computer Science, 2023, 50(6A): 220500001-6. |
| [3] | QIN Liang, XIE Liang, CHEN Shengshuang, XU Haijiao. Online Semi-supervised Cross-modal Hashing Based on Anchor Graph Classification [J]. Computer Science, 2023, 50(6): 183-193. |
| [4] | ZHANG Renbin, ZUO Yicong, ZHOU Zelin, WANG Long, CUI Yuhang. Multimodal Generative Adversarial Networks Based Multivariate Time Series Anomaly Detection [J]. Computer Science, 2023, 50(5): 355-362. |
| [5] | LI Haitao, WANG Ruimin, DONG Weiyu, JIANG Liehui. Semi-supervised Network Traffic Anomaly Detection Method Based on GRU [J]. Computer Science, 2023, 50(3): 380-390. |
| [6] | WANG Xiangwei, HAN Rui, Chi Harold LIU. Hierarchical Memory Pool Based Edge Semi-supervised Continual Learning Method [J]. Computer Science, 2023, 50(2): 23-31. |
| [7] | WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25. |
| [8] | HOU Xia-ye, CHEN Hai-yan, ZHANG Bing, YUAN Li-gang, JIA Yi-zhen. Active Metric Learning Based on Support Vector Machines [J]. Computer Science, 2022, 49(6A): 113-118. |
| [9] | WANG Yu-fei, CHEN Wen. Tri-training Algorithm Based on DECORATE Ensemble Learning and Credibility Assessment [J]. Computer Science, 2022, 49(6): 127-133. |
| [10] | XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo. Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [J]. Computer Science, 2022, 49(3): 288-293. |
| [11] | QIN Yue, DING Shi-fei. Survey of Semi-supervised Clustering [J]. Computer Science, 2019, 46(9): 15-21. |
| [12] | WU Zhen-yu, LI Yun-lei, WU Fan. Semi-supervised Support Tensor Based on Tucker Decomposition [J]. Computer Science, 2019, 46(9): 195-200. |
| [13] | SONG Xin,ZHU Zong-liang,GAO Yin-ping,CHANG Dao-fang. Vessel AIS Trajectory Online Compression Algorithm Combining Dynamic Thresholding and Global Optimization [J]. Computer Science, 2019, 46(7): 333-338. |
| [14] | SHEN Hong, LIU Jun-fa, CHEN Yi-qiang, JIANG Xin-long, HUANG Zheng-yu. Semi-supervised Scene Recognition Method Based on Multi-mode Fusion [J]. Computer Science, 2019, 46(12): 306-312. |
| [15] | YU Ying, CHEN Ke, SHOU Li-dan, CHEN Gang, WU Xiao-fan. Sentiment Analysis of User Comments Based on Extraction of Key Words and Key Sentences [J]. Computer Science, 2019, 46(10): 19-26. |
|
||
Home