{"title":"弥合差距:生成内部威胁数据的实用方法","authors":"Joshua Glasser, Brian Lindauer","doi":"10.1109/SPW.2013.37","DOIUrl":null,"url":null,"abstract":"The threat of malicious insider activity continues to be of paramount concern in both the public and private sectors. Though there is great interest in advancing the state of the art in predicting and stopping these threats, the difficulty of obtaining suitable data for research, development, and testing remains a significant hinderance. We outline the use of synthetic data to enable progress in one research program, while discussing the benefits and limitations of synthetic insider threat data, the meaning of realism in this context, as well as future research directions.","PeriodicalId":383569,"journal":{"name":"2013 IEEE Security and Privacy Workshops","volume":"293 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"202","resultStr":"{\"title\":\"Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data\",\"authors\":\"Joshua Glasser, Brian Lindauer\",\"doi\":\"10.1109/SPW.2013.37\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The threat of malicious insider activity continues to be of paramount concern in both the public and private sectors. Though there is great interest in advancing the state of the art in predicting and stopping these threats, the difficulty of obtaining suitable data for research, development, and testing remains a significant hinderance. We outline the use of synthetic data to enable progress in one research program, while discussing the benefits and limitations of synthetic insider threat data, the meaning of realism in this context, as well as future research directions.\",\"PeriodicalId\":383569,\"journal\":{\"name\":\"2013 IEEE Security and Privacy Workshops\",\"volume\":\"293 3\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"202\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Security and Privacy Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPW.2013.37\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Security and Privacy Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPW.2013.37","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data
The threat of malicious insider activity continues to be of paramount concern in both the public and private sectors. Though there is great interest in advancing the state of the art in predicting and stopping these threats, the difficulty of obtaining suitable data for research, development, and testing remains a significant hinderance. We outline the use of synthetic data to enable progress in one research program, while discussing the benefits and limitations of synthetic insider threat data, the meaning of realism in this context, as well as future research directions.