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Cyber Data Anomaly Detection Using Autoencoder Neural Networks, Spencer A. Butt
Cyber Data Anomaly Detection Using Autoencoder Neural Networks, Spencer A. Butt
Theses and Dissertations
The Department of Defense requires a secure presence in the cyber domain to successfully execute its stated mission of deterring war and protecting the security of the United States. With potentially millions of logged network events occurring on defended networks daily, a limited staff of cyber analysts require the capability to identify novel network actions for security adjudication. The detection methodology proposed uses an autoencoder neural network optimized via design of experiments for the identification of anomalous network events. Once trained, each logged network event is analyzed by the neural network and assigned an outlier score. The network events with …