Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Kennard R. Laviers, PhD.


A need for a quick response to cyber attacks is a prevalent problem for computer network operators today. There is a small window to respond to a cyber attack when it occurs to prevent significant damage to a computer network. Automated response planners offer one solution to resolve this issue. This work presents Network Defense Planner System (NDPS), a planner dependent on the effectiveness of the detection of the cyber attack. This research first explores making classification of network attacks faster for real-time detection, the basic function Intrusion Detection System (IDS) provides. After identifying the type of attack, learning the rewards to use in the NDPS is the second important area of this research. For NDPS to assemble the optimal plan, learning the rewards for resulting network states is critical and often depends on the preferences of the network operator. Using neural networks, the second area of this research demonstrates that capturing the preferences through samples is feasible. After training the neural network, a model can be created to obtain reward estimates. The research performed in these two areas complement the final portion of the research which is assembling the optimal plan through using the Upper Bounds on Confidence for Trees (UCT) algorithm. NDPS is implemented using the UCT algorithm which allows for quick plan formulation by searching through predicted network states based on available network actions. UCT can effectively create a plan quickly and is guaranteed to provide the optimal plan, according to rewards used, if enough time is allotted. NDPS is tested against eight random attack scenarios. For each attack scenario, the plan is polled at specific time intervals to test how quickly the optimal plan can be formulated. Results demonstrate the feasibility of NDPS to be used in real world scenarios since the optimal plans for each attack type can be formulated in real-time allowing for a rapid system response.

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