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 .

WITTEN I H , FRANK E , HALL M A , et al . Data mining:practical machine learning tools and techniques [M ] . Morgan Kaufmann , 2016 .

VAPNIK V N . An overview of statistical learning theory [J ] . IEEE transactions on neural networks , 1999 , 10 ( 5 ): 988 - 999 .

NASRABADI N M . Pattern recognition and machine learning [J ] . Journal of Electronic Imaging , 2007 , 16 ( 4 ):049901.

MITCHELL T M . Machine learning and data mining [J ] . Communications of the ACM , 1999 , 42 ( 11 ): 30 - 36 .

LECUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 521 ( 7553 ):436.

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [J ] . Communications of the ACM , 2012 , 60 ( 2 ): 2012 - 2025 .

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 .

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 .

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 .

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 .

CEARA D , POTET M L , ENSIMAG G I N P , et al . Detecting software vulnerabilities-static taint analysis [J ] . Polytechnic University of Bucharest , 2009 .

KING J C . Symbolic execution and program testing [J ] . Communications of the ACM , 1976 , 19 ( 7 ): 385 - 394 .

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 .

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 .

SUTTON M , GREENE A , AMINI P . Fuzzing:brute force vulnerability discovery [M ] . Pearson Education , 2007 .

NEWSOME J , SONG D . Dynamic taint analysis for automatic detection,analysis,and signature generation of exploits on commodity software [J ] . 2005 .

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 .

SURHONE L M , TENNOE M T , HENSSONOW S F , et al . Common vulnerabilities and exposures [M ] . Betascript Publishing , 2010 .

MELL P , SCARFONE K , ROMANOSKY S . Common vulnerability scoring system [J ] . IEEE Security ＆ Privacy , 2006 , 4 ( 6 ).

MCCABE T J . A complexity measure [J ] . IEEE Transactions on software Engineering , 1976 ( 4 ): 308 - 320 .

HALSTEAD M H . Elements of software science (operating and programming systems series) [M ] . Elsevier Science Inc , 1977 .

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 .

SHIN Y , WILLIAMS L . Can traditional fault prediction models be used for vulnerability prediction? [J ] . Empirical Software Engineering , 2013 , 18 ( 1 ): 25 - 59 .

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 .

SHIN Y , WILLIAMS L . Is complexity really the enemy of software security? [C ] // The 4th ACM Workshop on Quality of Protection . 2008 : 47 - 50 .

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 .

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 .

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 .

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 .

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 .

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.

MOSHTARI S , SAMI A , AZIMI M . Using complexity metrics to improve software security [J ] . Computer Fraud ＆ Security , 2013 , 2013 ( 5 ): 8 - 17 .

ALVES H , FONSECA B , ANTUNES N . Software metrics and security vulnerabilities:dataset and exploratory study [C ] // Dependable Computing Conference (EDCC) . 2016 : 37 - 44 .

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.

SCANDARIATO R , WALDEN J . Predicting vulnerable classes in an Android application [C ] // The 4th International Workshop on Security Measurements and Metrics . 2012 : 11 - 16 .

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 .

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 .

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 .

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 .

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.

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

PADMANABHUNI B M , TAN H B K . Auditing buffer overflow vulnerabilities using hybrid static-dynamic analysis [J ] . IET Software , 2016 , 10 ( 2 ): 54 - 61 .

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 .

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 .

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 .

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 .

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 .

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 .

DAM H K , TRAN T , PHAM T , et al . Automatic feature learning for vulnerability prediction [J ] . arXiv preprint,arXiv:1708.02368 , 2017 .

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 .

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 .

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 .

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 .

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 .

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 .

YAMAGUCHI F , LOTTMANN M , RIECK K . Generalized vulnerability extrapolation using abstract syntax trees [C ] // The 28th Annual Computer Security Applications Conference . 2012 : 359 - 368 .

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 .

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 .

MENG Q , WEN S , ZHANG B , et al . Automatically discover vulnerability through similar functions [C ] // Progress in Electromagnetic Research Symposium (PIERS) . 2016 : 3657 - 3661 .

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 .

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 .

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 .

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 .

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.

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 .

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 .

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 .

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 .

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 .

HEO K , OH H , YI K . Machine-learning-guided selectively unsound static analysis [C ] // The 39th International Conference on Software Engineering . 2017 : 519 - 529 .

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 .

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 .

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 .

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 .

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 .

MOU L , LI G , LIU Y , et al . Building program vector representations for deep learning [J ] . arXiv preprint,arXiv:1409.3358 , 2014 .

LI Z , ZOU D , XU S , et al . VulDeePecker:a deep learning-based system for vulnerability detection [J ] . arXiv preprint,arXiv:1801.01681 , 2018 .

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 .

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 .

LIN G , ZHANG J , LUO W , et al . Cross-project transfer representation learning for vulnerable function discovery [J ] . IEEE Transactions on Industrial Informatics , 2012 .

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 .

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 .

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 .

HU H , CHUA Z L , ADRIAN S , et al . Automatic generation of dataoriented exploits [C ] // USENIX Security Symposium . 2015 : 177 - 192 .

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 .

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 .

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 .

FELMETSGER V , CAVEDON L , KRUEGEl C , et al . Toward automated detection of logic vulnerabilities in Web applications [C ] // Usenix Security Symposium . 2010 : 143 - 160 .

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 .

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 .

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 .

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 .

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 .

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 .

SPANOS G , ANGELIS L , TOLOUDIS D . Assessment of vulnerability severity using text mining [C ] // Pan-Hellenic Conference on Informatics . 2017 : 1 - 6 .

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 .

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 .

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 .

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 .

ZHANG M , YIN H . AppSealer:automatic generation of vulnerability-specific patches for preventing component hijacking attacks in android applications [C ] // NDSS . 2014 .

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 .