Open Access. Powered by Scholars. Published by Universities.®
- Institution
Articles 1 - 3 of 3
Full-Text Articles in Computer Sciences
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale
Theses and Dissertations
Cyberspace is the digital communications network that supports the internet of battlefield things (IoBT), the model by which defense-centric sensors, computers, actuators and humans are digitally connected. A secure IoBT infrastructure facilitates real time implementation of the observe, orient, decide, act (OODA) loop across distributed subsystems. Successful hacking efforts by cyber criminals and strategic adversaries suggest that cyber systems such as the IoBT are not secure. Three lines of effort demonstrate a path towards a more robust IoBT. First, a baseline data set of enterprise cyber network traffic was collected and modelled with generative methods allowing the generation of realistic, …
Design And Analysis Of Strategic Behavior In Networks, Sixie Yu
Design And Analysis Of Strategic Behavior In Networks, Sixie Yu
McKelvey School of Engineering Theses & Dissertations
Networks permeate every aspect of our social and professional life.A networked system with strategic individuals can represent a variety of real-world scenarios with socioeconomic origins. In such a system, the individuals' utilities are interdependent---one individual's decision influences the decisions of others and vice versa. In order to gain insights into the system, the highly complicated interactions necessitate some level of abstraction. To capture the otherwise complex interactions, I use a game theoretic model called Networked Public Goods (NPG) game. I develop a computational framework based on NPGs to understand strategic individuals' behavior in networked systems. The framework consists of three …
Solving The Challenges Of Concept Drift In Data Stream Classification., Hanqing Hu
Solving The Challenges Of Concept Drift In Data Stream Classification., Hanqing Hu
Electronic Theses and Dissertations
The rise of network connected devices and applications leads to a significant increase in the volume of data that are continuously generated overtime time, called data streams. In real world applications, storing the entirety of a data stream for analyzing later is often not practical, due to the data stream’s potentially infinite volume. Data stream mining techniques and frameworks are therefore created to analyze streaming data as they arrive. However, compared to traditional data mining techniques, challenges unique to data stream mining also emerge, due to the high arrival rate of data streams and their dynamic nature. In this dissertation, …