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    Automated Cyber Vulnerability Analysis using Machine Reasoning

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    Author
    Sayan, Carla
    Issue Date
    2020
    Keywords
    cyber security
    machine reasoning
    Advisor
    Hariri, Salim
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Embargo
    Dissertation not available (per author's request)
    Abstract
    One of the major cyber security challenges we face today is overcoming the current limits of security information systems ability to improve an organization’s overall security posture and prioritize the security focus of an enterprise-level network. Latest developments in cyber security relating to the vulnerability of national assets, public utilities, and private industry have raised concern about threat response. The basic advantage to the cybercriminal is a reliance on the overwhelming number of attack vectors at their disposal. The amount of information presented to security analysts in large enterprises (e.g. thousands of machines, one million plus security incidents per day) is a challenging problem. This places analysts at a disadvantage of reactive security defense without a systematic approach to prioritize the security focus of a mission critical enterprise level network. Information presented, while abundant, lacks the context for arriving at knowledge to help determine critical node urgency, true priority of response, and the application of best courses of actions. To improve the security posture and assist the cyber defender, a new paradigm called Automated Cyber Vulnerability Analysis using Machine Reasoning has been implemented which captures relationships between security concepts and physical systems. The development of a Cyber Security Ontology and Cyber Security Infrastructure Ontology is outlined and the value of using ontology-based computational artifacts to support information integration and semantically reason about the impact of vulnerability and attacks on actual enterprise physical systems is demonstrated. We also implement scenarios that adopt a rule engine to enhance the machine reasoning ability of the ontology, resulting in new courses of actions and recommendations for the protection of physical enterprise systems. Our implementation is realized, and results show our approach as a powerful construct to improve security posture, offering a systematic approach to enhance cyber security decision support increasing the speed of analysis.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Electrical & Computer Engineering
    Degree Grantor
    University of Arizona
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