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dc.contributor.authorGalavotti, Christopher R.
dc.contributor.authorKahn, Russell; Thesis Advisor
dc.contributor.authorStam, Kathryn; Second Reader
dc.date.accessioned2020-02-10T18:06:38Z
dc.date.available2020-02-10T18:06:38Z
dc.date.issued2019-05
dc.identifier.urihttps://dspace.sunyconnect.suny.edu/handle/1951/71075
dc.descriptionA Research Thesis submitted to the College of Arts and Sciences at SUNY Polytechnic Institute, Utica, NY in partial fulfillment of the requirements for the degree of Master of Science in Information Design and Technology.en_US
dc.description.abstractNetwork posture has historically relied on traditional and reactionary methods for protection. These methods most commonly consist of network segmentation, intrusion detection systems, intrusion prevention systems, and signature-based detections. However, these traditional security platforms have proven to be an inadequate deterrent to the complex threat matrix that we currently find ourselves in. It is only through computational intelligence that we can truly identify potential intrusion areas and network abnormalities. This study presents a path forward for industry professionals on how to implement this computational approach into their network security platforms, particularly through stochastic modeling and simulation. Acknowledging the complex nature of this approach, a human-centered design methodology is also outlined on how to integrate this science into the enterprise via a predictive analytical dashboard.en_US
dc.language.isoen_USen_US
dc.subjectnetwork securityen_US
dc.subjectstochastic modelingen_US
dc.subjectattack graphsen_US
dc.subjectpredictive analyticsen_US
dc.subjectexploitability benefitsen_US
dc.subjectmarkovian chainsen_US
dc.subjectcomputationally intelligent systemsen_US
dc.subjectprinciples of designen_US
dc.subjecthuman-centered design theoryen_US
dc.subjectmodel simulation and accelerationen_US
dc.subjectsoftware development processen_US
dc.titleValidating Network Security with Predictive Analytics: A Design Guide to Bridge Stochastic Modeling into a Computationally Intelligent Dashboarden_US
dc.typeThesisen_US


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