［1］Ponta S, Plate H, Sabetta A. Detection, assessment and mitigation of vulnerabilities in open source dependencies［J］. Empirical Software Engineering, 2022, 7(25): 31753215［2］Morgan S. Cybercrime to cost the world $10.5 trillion annually by 2025［EBOL］.［20180922］. https:cybersecurityventures.comhackerpocalypsecybercrimereport2016［3］US Department of Homeland Security (DHS),Cybersecurity and Infrastructure Security Agency (CISA).CVE［EBOL］. ［20231026］. https:cve.mitre.org［4］Jin S, Wang Y, Cui X, et al. A review of classification methods for network vulnerability［C］ Proc of IEEE Int Conf on Systems. Piccataway, NJ: IEEE, 2009: 11711175［5］Wijayasekara D, Manic M, McQueen M. Vulnerability identification and classification via text mining bug databases［C］ Proc of the 40th Annual Conf of the IEEE Industrial Electronics Society. Piscataway, NJ: IEEE, 2014: 36123618［6］Davari M, Zulkernine M, Jaafar F. An automatic software vulnerability classification framework［C］ Proc of Int Conf on Software Security and Assurance (ICSSA). Piscataway, NJ: IEEE, 2017: 4449［7］Wang J A, Guo M. An ontology for vulnerability management［C］ Proc of the 5th Annual Workshop on Cyber Security and Information Intelligence Research Cyber Security and Information Intelligence Challenges and Strategies. New York: ACM, 2011: 23172322［8］US Department of Homeland Security (DHS), Cybersecurity and Infrastructure Security Agency (CISA). Common weakness enumeration［EBOL］. ［20231026］. https:cwe.mitre.org［9］Kowsari K, Jafari Meimandi K, Heidarysafa M, et al. Text classification algorithms: A survey［J］. arXiv preprint, arXiv:1904.08067, 2022［10］Chung J, Gulcehre C, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling［J］. arXiv preprint, arXiv:1412.3555, 2014［11］Neuhaus S, Zimmermann T. Security trend analysis with CVE topic models［C］ Proc of the 21st IEEE Int Symp on Software Reliability Engineering. Piscataway, NJ: IEEE, 2010: 111120［12］Na S, Kim T, Kim H. A study on the classification of common vulnerabilities and exposures using Nave Bayes［G］ Advances on BroadBand Wireless Computing,Communication and Applications. Berlin: Springer, 2022: 657662［13］Aota M, Kanehara H, Kubo M, et al. Automation of vulnerability classification from its description using machine learning［C］ Proc of IEEE Symp on Computers and Communications (ISCC). Piscataway, NJ: IEEE, 2020: 17［14］Aivatoglou G, Anastasiadis M, Spanos G, et al. A treebased machine learning methodology to automatically classify software vulnerabilities［C］ Proc of IEEE Int Conf on Cyber Security and Resilience (CSR). Piscataway, NJ: IEEE, 2019: 312317［15］Aghaei E, Alshaer E. ThreatZoom: Neural network for automated vulnerability mitigation［G］ LNICST 335: Proc of the 6th Annual Symp on Hot Topics in the Science of Security. Berlin: Springer, 2020: 2341［16］Das S, Serra E, Halappanavar M, et al. A framework for effective hierarchical multiclass classification of software vulnerabilities［C］ Proc of the 8th IEEE Int Conf on Data Science and Advanced Analytics (DSAA). Piscataway, NJ: IEEE, 2021: 112［17］Sun X, Li L, Bo L, et al. Automatic software vulnerability classification by extracting vulnerability triggers［J］. Journal of Software: Evolution and Process, 2022: 5(18): 5773［18］Wang Q, Gao Y, Ren J. An automatic classification algorithm for software vulnerability based on weighted word vector and fusion neural network［J］. Computers & Security, 2023:8(26):102107
|
