Degree Name
MS (Master of Science)
Program
Computer and Information Sciences
Date of Award
5-2020
Committee Chair or Co-Chairs
Mohammad S. Khan, Brian T. Bennett
Committee Members
Edward Hall, Matthew Harrison
Abstract
Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its design and the manner it is constructed, KFC holds great potential for implementation across a distributed system. The purpose of this thesis was to explain and extrapolate the afore mentioned IDS, highlight its effectiveness, and discuss the conceptual design of the distributed system for use in future research.
Document Type
Thesis - unrestricted
Recommended Citation
Schmidt, David, "Knot Flow Classification and its Applications in Vehicular Ad-Hoc Networks (VANET)" (2020). Electronic Theses and Dissertations. Paper 3723. https://dc.etsu.edu/etd/3723
Copyright
Copyright by the authors.
Included in
Artificial Intelligence and Robotics Commons, Information Security Commons, Numerical Analysis and Scientific Computing Commons, Other Computer Sciences Commons