Pub Date : 2024-02-07DOI: 10.1109/ICAIC60265.2024.10433837
Joseph Ponnoly, John Puthenveetil, Patricia D’Urso
Decision-making in cyber security attack scenarios involves deep uncertainty and adversarial decision-making. Robust Decision Making (RDM) uses a structured approach to evaluate the performance of various decision strategies under conditions of deep uncertainty to enable adaptive decision-making. The challenge is in getting reliable inputs to the RDM model. It is suggested that prescriptive analytics enabled by predictive analytics including big data analytics, reinforcement learning, and Monte Carlo simulations, could provide decision-makers with various options to make informed and robust decision-making. Prescriptive analytics based RDM model is proposed for cyber disaster risk reduction. The model extends the proactive cyber defense model based on sensing and sensemaking of early warning signs of cyber disasters.
{"title":"Prescriptive Analytics-based Robust Decision-Making Model for Cyber Disaster Risk Reduction","authors":"Joseph Ponnoly, John Puthenveetil, Patricia D’Urso","doi":"10.1109/ICAIC60265.2024.10433837","DOIUrl":"https://doi.org/10.1109/ICAIC60265.2024.10433837","url":null,"abstract":"Decision-making in cyber security attack scenarios involves deep uncertainty and adversarial decision-making. Robust Decision Making (RDM) uses a structured approach to evaluate the performance of various decision strategies under conditions of deep uncertainty to enable adaptive decision-making. The challenge is in getting reliable inputs to the RDM model. It is suggested that prescriptive analytics enabled by predictive analytics including big data analytics, reinforcement learning, and Monte Carlo simulations, could provide decision-makers with various options to make informed and robust decision-making. Prescriptive analytics based RDM model is proposed for cyber disaster risk reduction. The model extends the proactive cyber defense model based on sensing and sensemaking of early warning signs of cyber disasters.","PeriodicalId":517265,"journal":{"name":"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)","volume":"35 4","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：