Ioannis Agrafiotis, Arnau Erola, J. Happa, M. Goldsmith, S. Creese
{"title":"验证内部威胁检测系统:一个真实的场景视角","authors":"Ioannis Agrafiotis, Arnau Erola, J. Happa, M. Goldsmith, S. Creese","doi":"10.1109/SPW.2016.36","DOIUrl":null,"url":null,"abstract":"There exists unequivocal evidence denoting the dire consequences which organisations and governmental institutions face from insider threats. While the in-depth knowledge of the modus operandi that insiders possess provides ground for more sophisticated attacks, organisations are ill-equipped to detect and prevent these from happening. The research community has provided various models and detection systems to address the problem, but the lack of real data due to privacy and ethical issues remains a significant obstacle for validating and designing effective and scalable systems. In this paper, we present the results and our experiences from applying our detection system into a multinational organisation, the approach followed to abide with the ethical and privacy considerations and the lessons learnt on how the validation process refined the system in terms of effectiveness and scalability.","PeriodicalId":341207,"journal":{"name":"2016 IEEE Security and Privacy Workshops (SPW)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Validating an Insider Threat Detection System: A Real Scenario Perspective\",\"authors\":\"Ioannis Agrafiotis, Arnau Erola, J. Happa, M. Goldsmith, S. Creese\",\"doi\":\"10.1109/SPW.2016.36\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There exists unequivocal evidence denoting the dire consequences which organisations and governmental institutions face from insider threats. While the in-depth knowledge of the modus operandi that insiders possess provides ground for more sophisticated attacks, organisations are ill-equipped to detect and prevent these from happening. The research community has provided various models and detection systems to address the problem, but the lack of real data due to privacy and ethical issues remains a significant obstacle for validating and designing effective and scalable systems. In this paper, we present the results and our experiences from applying our detection system into a multinational organisation, the approach followed to abide with the ethical and privacy considerations and the lessons learnt on how the validation process refined the system in terms of effectiveness and scalability.\",\"PeriodicalId\":341207,\"journal\":{\"name\":\"2016 IEEE Security and Privacy Workshops (SPW)\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Security and Privacy Workshops (SPW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPW.2016.36\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Security and Privacy Workshops (SPW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPW.2016.36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Validating an Insider Threat Detection System: A Real Scenario Perspective
There exists unequivocal evidence denoting the dire consequences which organisations and governmental institutions face from insider threats. While the in-depth knowledge of the modus operandi that insiders possess provides ground for more sophisticated attacks, organisations are ill-equipped to detect and prevent these from happening. The research community has provided various models and detection systems to address the problem, but the lack of real data due to privacy and ethical issues remains a significant obstacle for validating and designing effective and scalable systems. In this paper, we present the results and our experiences from applying our detection system into a multinational organisation, the approach followed to abide with the ethical and privacy considerations and the lessons learnt on how the validation process refined the system in terms of effectiveness and scalability.