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    Validating Network Security with Predictive Analytics: A Design Guide to Bridge Stochastic Modeling into a Computationally Intelligent Dashboard

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    Author
    Galavotti, Christopher R.
    Kahn, Russell; Thesis Advisor
    Stam, Kathryn; Second Reader
    Keyword
    network security
    stochastic modeling
    attack graphs
    predictive analytics
    exploitability benefits
    markovian chains
    computationally intelligent systems
    principles of design
    human-centered design theory
    model simulation and acceleration
    software development process
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    Date Published
    2019-05
    
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    URI
    http://hdl.handle.net/20.500.12648/929
    Abstract
    Network 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.
    Description
    A 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.
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    SUNY Polytechnic Institute Information Design + Technology (IDT) Program Theses and Projects

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