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Full-Text Articles in Physical Sciences and Mathematics
Learning-Based Analysis On The Exploitability Of Security Vulnerabilities, Adam Bliss
Learning-Based Analysis On The Exploitability Of Security Vulnerabilities, Adam Bliss
Computer Science and Computer Engineering Undergraduate Honors Theses
The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing …
Parameterizing And Aggregating Activation Functions In Deep Neural Networks, Luke Benjamin Godfrey
Parameterizing And Aggregating Activation Functions In Deep Neural Networks, Luke Benjamin Godfrey
Graduate Theses and Dissertations
The nonlinear activation functions applied by each neuron in a neural network are essential for making neural networks powerful representational models. If these are omitted, even deep neural networks reduce to simple linear regression due to the fact that a linear combination of linear combinations is still a linear combination. In much of the existing literature on neural networks, just one or two activation functions are selected for the entire network, even though the use of heterogenous activation functions has been shown to produce superior results in some cases. Even less often employed are activation functions that can adapt their …