Journal on Communications ›› 2018, Vol. 39 ›› Issue (8): 1-17.doi: 10.11959/j.issn.1000-436x.2018137
• Artificial Intelligence and Network Security • Next Articles
Hongyu SUN1,2,Yuan HE2,Jice WANG2,Ying DONG2,Lipeng ZHU1,2,He WANG1,2,Yuqing ZHANG1,2
Revised:
2018-07-20
Online:
2018-08-01
Published:
2018-09-13
Supported by:
CLC Number:
Hongyu SUN,Yuan HE,Jice WANG,Ying DONG,Lipeng ZHU,He WANG,Yuqing ZHANG. Application of artificial intelligence technology in the field of security vulnerability[J]. Journal on Communications, 2018, 39(8): 1-17.
[50] | PADMANABHUNI B M , TAN H B K . Predicting buffer overflow vulnerabilities through mining light-weight static code attributes[C]// 2014 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). 2014: 317-322. |
[51] | PADMANABHUNI B M , TAN H B K . Auditing buffer overflow vulnerabilities using hybrid static-dynamic analysis[J]. IET Software, 2016,10(2): 54-61. |
[52] | MENG Q , ZHANG B , FENG C ,et al. Detecting buffer boundary violations based on SVM[C]// 2016 3rd International Conference on Information Science and Control Engineering (ICISCE). 2016: 313-316. |
[53] | MENG Q , WEN S , FENG C ,et al. Predicting integer overflow through static integer operation attributes[C]// International Conference on Computer Science and Network Technology. 2017: 177-181. |
[54] | WANG D , LIN M , ZHANG H ,et al. Detect related bugs from source code using bug information[C]// Computer Software and Applications Conference (COMPSAC). 2010: 228-237. |
[55] | HOVSEPYAN A , SCANDARIATO R , JOOSEN W ,et al. Software vulnerability prediction using text analysis techniques[C]// The 4th International Workshop on Security Measurements and Metrics. 2012: 7-10. |
[56] | SCANDARIATO R , WALDEN J , HOVSEPYAN A ,et al. Predicting vulnerable software components via text mining[J]. IEEE Transactions on Software Engineering, 2014,40(10): 993-1006. |
[57] | PANG Y , XUE X , NAMIN A S . Early identification of vulnerable software components via ensemble learning[C]// 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). 2016: 476-481. |
[58] | DAM H K , TRAN T , PHAM T ,et al. Automatic feature learning for vulnerability prediction[J]. arXiv preprint,arXiv:1708.02368, 2017. |
[59] | PANG Y , XUE X , NAMIN A S . Predicting vulnerable software components through n-gram analysis and statistical feature selection[C]// 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). 2015: 543-548. |
[60] | PANG Y , XUE X , WANG H . Predicting vulnerable software components through deep neural network[C]// The 2017 International Conference on Deep Learning Technologies. 2017: 6-10. |
[61] | STUCKMAN J , WALDEN J , SCANDARIATO R . The effect of dimensionality reduction on software vulnerability prediction models[J]. IEEE Transactions on Reliability, 2017,66(1): 17-37. |
[62] | WIJAYASEKARA D , MANIC M , WRIGHT J L ,et al. Mining bug databases for unidentified software vulnerabilities[C]// 2012 5th International Conference on Human System Interactions (HSI). 2012: 89-96. |
[63] | WIJAYASEKARA D , MANIC M , MCQUEEN M . Information gain based dimensionality selection for classifying text documents[C]// 2013 IEEE Congress on Evolutionary Computation (CEC). 2013: 440-445. |
[64] | WIJAYASEKARA D , MANIC M , MCQUEEN M . Vulnerability identification and classification via text mining bug databases[C]// Industrial Electronics Society,IECON 2014-40th Annual Conference of the IEEE. 2014: 3612-3618. |
[65] | YAMAGUCHI F , LOTTMANN M , RIECK K . Generalized vulnerability extrapolation using abstract syntax trees[C]// The 28th Annual Computer Security Applications Conference. 2012: 359-368. |
[66] | YAMAGUCHI F , WRESSNEGGER C , GASCON H ,et al. Chucky:exposing missing checks in source code for vulnerability discovery[C]// The 2013 ACM SIGSAC Conference on Computer & Communications Security. 2013: 499-510. |
[67] | YAMAGUCHI F , MAIER A , GASCON H ,et al. Automatic inference of search patterns for taint-style vulnerabilities[C]// 2015 IEEE Symposium on Security and Privacy (SP). 2015: 797-812. |
[68] | MENG Q , WEN S , ZHANG B ,et al. Automatically discover vulnerability through similar functions[C]// Progress in Electromagnetic Research Symposium (PIERS). 2016: 3657-3661. |
[69] | MEDEIROS I , NEVES N , CORREIA M . Detecting and removing web application vulnerabilities with static analysis and data mining[J]. IEEE Transactions on Reliability, 2016,65(1): 54-69. |
[70] | MENG Q , SHAMENG W , CHAO F ,et al. Predicting buffer overflow using semi-supervised learning[C]// International Congress on Image and Signal Processing,BioMedical Engineering and Informatics (CISP-BMEI), 2016: 1959-1963. |
[71] | ALOHALY M , TAKABI H . When do changes induce software vulnerabilities?[C]// 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC). 2017: 59-66. |
[72] | NEUHAUS S , ZIMMERMANN T , HOLLER C ,et al. Predicting vulnerable software components[C]// The 14th ACM Conference on Computer and Communications Security. 2007: 529-540. |
[73] | YAMAGUCHI F , LINDNER F , RIECK K . Vulnerability extrapolation:assisted discovery of vulnerabilities using machine learning[C]// The 5th USENIX Conference on Offensive Technologies. 2011:13. |
[74] | WALDEN J , STUCKMAN J , SCANDARIATO R . Predicting vulnerable components:software metrics vs text mining[C]// 2014 IEEE 25th International Symposium on Software Reliability Engineering (ISSRE). 2014: 23-33. |
[75] | TANG Y , ZHAO F , YANG Y ,et al. Predicting vulnerable components via text mining or software metrics? an effort-aware perspective[C]// 2015 IEEE International Conference on Software Quality,Reliability and Security (QRS). 2015: 27-36. |
[1] | 张玉清, 宫亚峰, 王宏 ,等. 安全漏洞标识与描述规范[S].. GB/T28458-2012,全国信息安全标准化技术委员会(SAC/TC 260). |
ZHANG Y Q , GONG Y F , WANG H ,et al. Vulnerability identification and description specification[S]. GB/T28458-2012,National Information Security Standardization Technical Committee. | |
[76] | ZHANG Y , LO D , XIA X ,et al. Combining software metrics and text features for vulnerable file prediction[C]// 2015 20th International Conference on Engineering of Complex Computer Systems (ICECCS). 2015: 40-49. |
[77] | MENG Q , ZHANG B , FENG C ,et al. Detecting buffer boundary violations based on SVM[C]// 2016 3rd International Conference on Information Science and Control Engineering (ICISCE). 2016: 313-316. |
[2] | WITTEN I H , FRANK E , HALL M A ,et al. Data mining:practical machine learning tools and techniques[M]. Morgan Kaufmann, 2016. |
[3] | VAPNIK V N . An overview of statistical learning theory[J]. IEEE transactions on neural networks, 1999,10(5): 988-999. |
[78] | MEDEIROS I , NEVES N F , CORREIA M . Automatic detection and correction of web application vulnerabilities using data mining to predict false positives[C]// The 23rd International Conference on World Wide Web. 2014: 63-74. |
[79] | HEO K , OH H , YI K . Machine-learning-guided selectively unsound static analysis[C]// The 39th International Conference on Software Engineering. 2017: 519-529. |
[4] | NASRABADI N M . Pattern recognition and machine learning[J]. Journal of Electronic Imaging, 2007,16(4):049901. |
[5] | MITCHELL T M . Machine learning and data mining[J]. Communications of the ACM, 1999,42(11): 30-36. |
[80] | GRIECO G , GRINBLAT G L , UZAL L ,et al. Toward large-scale vulnerability discovery using machine learning[C]// The Sixth ACM Conference on Data and Application Security and Privacy. 2016: 85-96. |
[81] | GODEFROID P , PELEG H , SINGH R . Learn&fuzz:machine learning for input fuzzing[C]// The 32nd IEEE/ACM International Conference on Automated Software Engineering. 2017: 50-59. |
[6] | LECUN Y , BENGIO Y , HINTON G . Deep learning[J]. Nature, 2015,521(7553):436. |
[7] | KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2012,60(2): 2012-2025. |
[82] | LESSMANN S , BAESENS B , MUES C ,et al. Benchmarking classification models for software defect prediction:a proposed framework and novel findings[J]. IEEE Transactions on Software Engineering, 2008,34(4): 485-496. |
[83] | GHOTRA B , MCINTOSH S , HASSAN A E . Revisiting the impact of classification techniques on the performance of defect prediction models[C]// The 37th International Conference on Software Engineering. 2015: 789-800. |
[8] | TAIGMAN Y , YANG M , RANZATO M A ,et al. Deepface:closing the gap to human-level performance in face verification[C]// The 29th IEEE Conference on Computer Vision and Pattern Recognition. 2014: 1701-1708. |
[9] | COLLOBERT R , WESTON J . A unified architecture for natural language processing:deep neural networks with multitask learning[C]// The 25th International Conference on Machine Learning. 2008: 160-167. |
[84] | TANTITHAMTHAVORN C , MCINTOSH S , HASSAN A E ,et al. Automated parameter optimization of classification techniques for defect prediction models[C]// 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE). 2016: 321-332. |
[85] | MOU L , LI G , LIU Y ,et al. Building program vector representations for deep learning[J]. arXiv preprint,arXiv:1409.3358, 2014. |
[10] | HUANG W Y , STOKES J W . MtNet:a multi-task neural network for dynamic malware classification[C]// The 5th 25th International Conference on Detection of Intrusions and Malware,and Vulnerability Assessment. 2016: 399-418. |
[11] | DEBAR H , BECKER M , SIBONI D . A neural network component for an intrusion detection system[C]// The 23rd Computer Society Symp on Research in Security and Privacy. 1992: 240-250. |
[86] | LI Z , ZOU D , XU S ,et al. VulDeePecker:a deep learning-based system for vulnerability detection[J]. arXiv preprint,arXiv:1801.01681, 2018. |
[87] | WU F , WANG J , LIU J ,et al. Vulnerability detection with deep learning[C]// 2017 3rd IEEE International Conference on Computer and Communications (ICCC). 2017: 1298-1302. |
[12] | CEARA D , POTET M L , ENSIMAG G I N P ,et al. Detecting software vulnerabilities-static taint analysis[J]. Polytechnic University of Bucharest, 2009. |
[13] | KING J C . Symbolic execution and program testing[J]. Communications of the ACM, 1976,19(7): 385-394. |
[88] | MA Y , LUO G , ZENG X ,et al. Transfer learning for cross-company software defect prediction[J]. Information and Software Technology, 2012,54(3): 248-256. |
[89] | LIN G , ZHANG J , LUO W ,et al. Cross-project transfer representation learning for vulnerable function discovery[J]. IEEE Transactions on Industrial Informatics, 2012. |
[14] | GAO D , REITER M K , SONG D . Binhunt:automatically finding semantic differences in binary programs[C]// International Conference on Information and Communications Security. 2008: 238-255. |
[15] | DUKES L S , YUAN X , AKOWUAH F . A case study on web application security testing with tools and manual testing[C]// 2013 Proceedings of IEEE. 2013: 1-6. |
[90] | BRUMLEY D , POOSANKAM P , SONG D ,et al. Automatic patch-based exploit generation is possible:techniques and implications[C]// IEEE Symposium on Security and Privacy. 2008: 143-157. |
[91] | CHA S K , AVGERINOS T , REBERT A ,et al. Unleashing mayhem on binary code[C]// 2012 IEEE Symposium on Security and Privacy (SP). 2012: 380-394. |
[16] | SUTTON M , GREENE A , AMINI P . Fuzzing:brute force vulnerability discovery[M]. Pearson Education, 2007. |
[17] | NEWSOME J , SONG D . Dynamic taint analysis for automatic detection,analysis,and signature generation of exploits on commodity software[J]. 2005. |
[92] | WANG M , SU P , LI Q ,et al. Automatic polymorphic exploit generation for software vulnerabilities[C]// International Conference on Security and Privacy in Communication Systems. 2013: 216-233. |
[93] | HU H , CHUA Z L , ADRIAN S ,et al. Automatic generation of dataoriented exploits[C]// USENIX Security Symposium. 2015: 177-192. |
[18] | XIE T , TILLMANN N , DE H J ,et al. Fitness-guided path exploration in dynamic symbolic execution[C]// IEEE/IFIP International Conference on Dependable Systems & Networks. 2009: 359-368. |
[19] | SURHONE L M , TENNOE M T , HENSSONOW S F ,et al. Common vulnerabilities and exposures[M]. Betascript Publishing, 2010. |
[94] | BAO T , WANG R , SHOSHITAISHVILI Y ,et al. Your exploit is mine:automatic shellcode transplant for remote exploits[C]// 2017 IEEE Symposium on Security and Privacy (SP). 2017: 824-839. |
[95] | ALHUZALI A , ESHETE B , GJOMEMO R ,et al. Chainsaw:chained automated workflow-based exploit generation[C]// ACM Sigsac Conference on Computer and Communications Security. 2016: 641-652. |
[20] | MELL P , SCARFONE K , ROMANOSKY S . Common vulnerability scoring system[J]. IEEE Security & Privacy, 2006,4(6). |
[21] | MCCABE T J . A complexity measure[J]. IEEE Transactions on software Engineering, 1976(4): 308-320. |
[96] | HUANG S K , LU H L , LEONG W M ,et al. CRAXweb:automatic web application testing and attack generation[C]// IEEE,International Conference on Software Security and Reliability. 2013: 208-217. |
[97] | FELMETSGER V , CAVEDON L , KRUEGEl C ,et al. Toward automated detection of logic vulnerabilities in Web applications[C]// Usenix Security Symposium. 2010: 143-160. |
[22] | HALSTEAD M H . Elements of software science (operating and programming systems series)[M]. Elsevier Science Inc, 1977. |
[23] | ZIMMERMANN T , NAGAPPAN N , WILLIAMS L . Searching for a needle in a haystack:predicting security vulnerabilities for windows vista[C]// 2010 Third International Conference on Software Testing,Verification and Validation (ICST). 2010: 421-428. |
[98] | LUO L , ZENG Q , CAO C ,et al. System service call-oriented symbolic execution of android framework with applications to vulnerability discovery and exploit generation[C]// The 15th Annual International Conference on Mobile Systems,Applications,and Services. 2017: 225-238. |
[99] | YOU W , ZONG P , CHEN K ,et al. SemFuzz:semantics-based automatic generation of proof-of-concept exploits[C]// The 2017 ACM SIGSAC Conference on Computer and Communications Security. 2017: 2139-2154. |
[24] | SHIN Y , WILLIAMS L . Can traditional fault prediction models be used for vulnerability prediction?[J]. Empirical Software Engineering, 2013,18(1): 25-59. |
[25] | SHIN Y , WILLIAMS L . An empirical model to predict security vulnerabilities using code complexity metrics[C]// The Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. 2008: 315-317. |
[100] | YOUNIS A , MALAIYA Y , ANDERSON C ,et al. To fear or not to fear that is the question:code characteristics of a vulnerable functionwith an existing exploit[C]// The Sixth ACM Conference on Data and Application Security and Privacy. 2016: 97-104. |
[101] | BOZORGI M , SAUL L K , SAVAGE S ,et al. Beyond heuristics:learning to classify vulnerabilities and predict exploits[C]// The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010: 105-114. |
[26] | SHIN Y , WILLIAMS L . Is complexity really the enemy of software security?[C]// The 4th ACM Workshop on Quality of Protection. 2008: 47-50. |
[27] | SHIN Y , WILLIAMS L . An initial study on the use of execution complexity metrics as indicators of software vulnerabilities[C]// The 7th International Workshop on Software Engineering for Secure Systems. 2011: 1-7. |
[102] | ALLODI L , MASSACCI F . A preliminary analysis of vulnerability scores for attacks in wild:the ekits and sym datasets[C]// The 2012 ACM Workshop on Building analysis datasets and gathering experience returns for security. 2012: 17-24. |
[103] | YAMAMOTO Y , MIYAMOTO D , NAKAYAMA M . Text-mining approach for estimating vulnerability score[C]// International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security. 2017: 67-73. |
[28] | DOYLE M , WALDEN J . An empirical study of the evolution of PHP web application security[C]// 2011 Third International Workshop on Security Measurements and Metrics (Metrisec). 2011: 11-20. |
[29] | CHOWDHURY I , ZULKERNINE M . Using complexity,coupling,and cohesion metrics as early indicators of vulnerabilities[J]. Journal of Systems Architecture, 2011,57(3): 294-313. |
[104] | SPANOS G , ANGELIS L , TOLOUDIS D . Assessment of vulnerability severity using text mining[C]// Pan-Hellenic Conference on Informatics. 2017: 1-6. |
[105] | HAN Z , LI X , XING Z ,et al. Learning to predict severity of software vulnerability using only vulnerability description[C]// 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). 2017: 125-136. |
[30] | CHOWDHURY I , ZULKERNINE M . Can complexity,coupling,and cohesion metrics be used as early indicators of vulnerabilities?[C]// The 2010 ACM Symposium on Applied Computing. 2010: 1963-1969. |
[31] | SHIN Y , MENEELY A , WILLIAMS L ,et al. Evaluating complexity,code churn,and developer activity metrics as indicators of software vulnerabilities[J]. IEEE Transactions on Software Engineering, 2011,37(6): 772-787. |
[106] | ZHANG C , WANG T , WEI T ,et al. IntPatch:automatically fix integer-overflow-to-buffer-overflow vulnerability at compile-time[C]// European Symposium on Research in Computer Security. 2010: 71-86. |
[107] | LE G C , NGUYEN T V , FORREST S ,et al. Genprog:a generic method for automatic software repair[J]. IEEE Transactions On Software Engineering, 2012,38(1): 54-72. |
[32] | MENEELY A , WILLIAMS L . Strengthening the empirical analysis of the relationship between Linus’ Law and software security[C]// The 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. 2010:9. |
[33] | MOSHTARI S , SAMI A , AZIMI M . Using complexity metrics to improve software security[J]. Computer Fraud & Security, 2013,2013(5): 8-17. |
[108] | WHITE M , TUFANO M , MARTINEZ M ,et al. Sorting and transforming program repair ingredients via deep learning code similarities[J]. arXiv preprint,arXiv:1707.04742, 2017. |
[109] | ZHANG M , YIN H . AppSealer:automatic generation of vulnerability-specific patches for preventing component hijacking attacks in android applications[C]// NDSS. 2014. |
[34] | ALVES H , FONSECA B , ANTUNES N . Software metrics and security vulnerabilities:dataset and exploratory study[C]// Dependable Computing Conference (EDCC). 2016: 37-44. |
[35] | MORRISON P , HERZIG K , MURPHY B ,et al. Challenges with applying vulnerability prediction models[C]// The 2015 Symposium and Bootcamp on the Science of Security. 2015:4. |
[36] | SCANDARIATO R , WALDEN J . Predicting vulnerable classes in an Android application[C]// The 4th International Workshop on Security Measurements and Metrics. 2012: 11-16. |
[37] | MENEELY A , SRINIVASAN H , MUSA A ,et al. When a patch goes bad:Exploring the properties of vulnerability-contributing commits[C]// 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. 2013: 65-74. |
[38] | PERL H , DECHAND S , SMITH M ,et al. Vccfinder:finding potential vulnerabilities in open-source projects to assist code audits[C]// The 22nd ACM SIGSAC Conference on Computer and Communications Security. 2015: 426-437. |
[39] | YANG L , LI X , YU Y . VulDigger:a just-in-time and cost-aware tool for digging vulnerability-contributing changes[C]// 2017 IEEE Global Communications Conference. 2017: 1-7. |
[40] | GEGICK M , WILLIAMS L , OSBORNE J ,et al. Prioritizing software security fortification throughcode-level metrics[C]// The 4th ACM workshop on Quality of Protection. 2008: 31-38. |
[41] | NGUYEN V H , TRAN L M S . Predicting vulnerable software components with dependency graphs[C]// The 6th International Workshop on Security Measurements and Metrics. 2010:3. |
[42] | YAN H , SUI Y , CHEN S ,et al. Machine-learning-guided typestate analysis for static use-after-free detection[C]// The 33rd Annual Computer Security Applications Conference. 2017: 42-54. |
[43] | GUPTA M K , GOVIL M C , SINGH G . Predicting cross-site scripting (XSS) security vulnerabilities in web applications[C]// 2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE). 2015: 162-167. |
[44] | ZHANG S , CARAGEA D , OU X . An empirical study on using the national vulnerability database to predict software vulnerabilities[C]// International Conference on Database and Expert Systems Applications. 2011: 217-231. |
[45] | SHAR L K , TAN H B K . Predicting common web application vulnerabilities from input validation and sanitization code patterns[C]// The 27th IEEE/ACM International Conference on Automated Software Engineering (ASE). 2012: 310-313. |
[46] | SHAR L K , TAN H B K . Predicting SQL injection and cross site scripting vulnerabilities through mining input sanitization patterns[J]. Information and Software Technology, 2013,55(10): 1767-1780. |
[47] | SHAR L K , TAN H B K , BRIAND L C . Mining SQL injection and cross site scripting vulnerabilities using hybrid program analysis[C]// The 2013 International Conference on Software Engineering. 2013: 642-651. |
[48] | SHAR L K , BRIAND L C , TAN H B K . Web application vulnerability prediction using hybrid program analysis and machine learning[J]. IEEE Transactions on Dependable and Secure Computing, 2015,12(6): 688-707. |
[110] | BEN O L , CHEHRAZI G , BODDEN E ,et al. Factors impacting the effort required to fix security vulnerabilities[C]// International Information Security Conference. 2015: 102-119. |
[49] | PADMANABHUNI B M , TAN H B K . buffer overflow vulnerability prediction from x86 executables using static analysis and machine learning[C]// Computer Software and Applications Conference (COMPSAC). 2015: 450-459. |
[1] | Qianyi DAI, Bin ZHANG, Song GUO, Kaiyong XU. Blockchain network layer anomaly traffic detection method based on multiple classifier integration [J]. Journal on Communications, 2023, 44(3): 66-80. |
[2] | Feibo JIANG, Yubo PENG, Li DONG. Deep image semantic communication model for 6G [J]. Journal on Communications, 2023, 44(3): 198-208. |
[3] | Jingyu WANG, Zirui ZHUANG. Research on a knowledge-defined polymorphic network attainable service architecture [J]. Journal on Communications, 2022, 43(4): 71-82. |
[4] | Zhisheng NIU, Sheng ZHOU, Yuxuan SUN. Green communication and networking for Carbon-peaking and Carbon-neutrality: challenges and solutions [J]. Journal on Communications, 2022, 43(2): 1-14. |
[5] | Gaofeng HE, Qianfeng WEI, Xiancai XIAO, Haiting ZHU, Bingfeng XU. Confirmation method for the detection of malicious encrypted traffic with data privacy protection [J]. Journal on Communications, 2022, 43(2): 156-170. |
[6] | Zhibin FENG, Yuhua XU, Zhiyong DU, Xin LIU, Wen LI, Hao HAN, Xiaobo ZHANG. Active defense technology against intelligent jammer [J]. Journal on Communications, 2022, 43(10): 42-54. |
[7] | Yanhui LU, Han LIU, Hang LI, Guangxu ZHU. Time series generation model based on multi-discriminator generative adversarial network [J]. Journal on Communications, 2022, 43(10): 167-176. |
[8] | Kai MEI, Haitao ZHAO, Xiaoran LIU, Jun LIU, Jun XIONG, Baoquan REN, Jibo WEI. Efficient model-and-data based channel estimation algorithm [J]. Journal on Communications, 2022, 43(1): 59-70. |
[9] | Changgen PENG, Ting GAO, Huilan LIU, Hongfa DING. PCA-based membership inference attack for machine learning models [J]. Journal on Communications, 2022, 43(1): 149-160. |
[10] | Futai ZOU, Yue TAN, Lin WANG, Yongkang JIANG. Botnet detection based on generative adversarial network [J]. Journal on Communications, 2021, 42(7): 95-106. |
[11] | Siya XU, Yifei XING, Shaoyong GUO, Chao YANG, Xuesong QIU, Luoming MENG. Deep reinforcement learning based task allocation mechanism for intelligent inspection in energy Internet [J]. Journal on Communications, 2021, 42(5): 191-204. |
[12] | Liu LIU, Jianhua ZHANG, Yuanyuan FAN, Li YU, Jiachi ZHANG. Survey of application of machine learning in wireless channel modeling [J]. Journal on Communications, 2021, 42(2): 134-153. |
[13] | Chuanhong LIU, Caili GUO, Yang YANG, Chunyan FENG, Qizheng SUN, Jiujiu CHEN. Intelligent task-oriented semantic communication method in artificial intelligence of things [J]. Journal on Communications, 2021, 42(11): 97-108. |
[14] | Tao HUANG, Jiang LIU, Shuo WANG, Chen ZHANG, Yunjie LIU. Survey of the future network technology and trend [J]. Journal on Communications, 2021, 42(1): 130-150. |
[15] | Yusun FU,Genke YANG. Application of artificial intelligence in mobile communication:challenge and practice [J]. Journal on Communications, 2020, 41(9): 190-201. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|