The research problem that inspired this effort is the challenge of managing the security of systems in large-scale heterogeneous networked environments. Human intervention is slow and limited: humans operate at much slower speeds than networked computer communications and there are few humans associated with each network. Enabling each node in the network to assist in this security management challenge is not a total solution to the problem but could potentially contribute to making the challenge more tractable. Specifically, this research effort is focused on enabling each node to contribute to the security management challenge by creating a capability at each node that continually assesses the operational environment and makes decisions based on that assessment regarding its operational profile. The decision process is based on two elements: input from near-neighbor nodes and self-assessment based on a priori conditional states. The end product of this decision space is a nervousness index which is used by each node to increase or decrease operational patterns of behavior. This process assists in the security management of the network by automatically reducing operational capabilities based on group environmental assessments, which provides the human administrator valuable input in the overall security state of the network as well as additional time to react when threats are detected in the networked environment.
|Advisor:||Ryan, Julie J.C.H.|
|Commitee:||Duffey, Michael R., Mazzuchi, Thomas A., Ozment, James A., Rene van Dorp, Johan|
|School:||The George Washington University|
|Department:||Engineering Mgt and Systems Engineering|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 74/05(E), Dissertation Abstracts International|
|Subjects:||Information Technology, Systems science|
|Keywords:||Distributed artificial intelligence, Emerging technologies, Information system management, Network defenses, Network-level security and protection|
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