{"title":"基于特征的机器学习漏洞预测特征选择研究","authors":"Zhanjun Li, Yan Shao","doi":"10.1145/3318299.3318345","DOIUrl":null,"url":null,"abstract":"This paper summarized the basic process of software vulnerability prediction using feature-based machine learning for the first time. In addition to sorting out the related types and basis of vulnerability features definition, the advantages and disadvantages of different methods are compared. Finally, this paper analyzed the difficulties and challenges in this research field, and put forward some suggestions for future work.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A Survey of Feature Selection for Vulnerability Prediction Using Feature-based Machine Learning\",\"authors\":\"Zhanjun Li, Yan Shao\",\"doi\":\"10.1145/3318299.3318345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper summarized the basic process of software vulnerability prediction using feature-based machine learning for the first time. In addition to sorting out the related types and basis of vulnerability features definition, the advantages and disadvantages of different methods are compared. Finally, this paper analyzed the difficulties and challenges in this research field, and put forward some suggestions for future work.\",\"PeriodicalId\":164987,\"journal\":{\"name\":\"International Conference on Machine Learning and Computing\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Machine Learning and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3318299.3318345\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Machine Learning and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3318299.3318345","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Survey of Feature Selection for Vulnerability Prediction Using Feature-based Machine Learning
This paper summarized the basic process of software vulnerability prediction using feature-based machine learning for the first time. In addition to sorting out the related types and basis of vulnerability features definition, the advantages and disadvantages of different methods are compared. Finally, this paper analyzed the difficulties and challenges in this research field, and put forward some suggestions for future work.