{"title":"一种提高自动生成库存质量的半自动方法","authors":"Silvia Bonomi, Marco Cuoci, S. Lenti","doi":"10.1109/CSR57506.2023.10225003","DOIUrl":null,"url":null,"abstract":"Inventories are precious sources of information for security-related processes. As a consequence, the quality of the data in the inventories plays a crucial role in the overall quality of the fed processes. This paper takes this challenge and provides heuristics to improve the accuracy of automatically generated inventories through a semi-automatic approach leveraging user knowledge.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Semi-automatic Approach for Enhancing the Quality of Automatically Generated Inventories\",\"authors\":\"Silvia Bonomi, Marco Cuoci, S. Lenti\",\"doi\":\"10.1109/CSR57506.2023.10225003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Inventories are precious sources of information for security-related processes. As a consequence, the quality of the data in the inventories plays a crucial role in the overall quality of the fed processes. This paper takes this challenge and provides heuristics to improve the accuracy of automatically generated inventories through a semi-automatic approach leveraging user knowledge.\",\"PeriodicalId\":354918,\"journal\":{\"name\":\"2023 IEEE International Conference on Cyber Security and Resilience (CSR)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE International Conference on Cyber Security and Resilience (CSR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSR57506.2023.10225003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSR57506.2023.10225003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Semi-automatic Approach for Enhancing the Quality of Automatically Generated Inventories
Inventories are precious sources of information for security-related processes. As a consequence, the quality of the data in the inventories plays a crucial role in the overall quality of the fed processes. This paper takes this challenge and provides heuristics to improve the accuracy of automatically generated inventories through a semi-automatic approach leveraging user knowledge.