MS (Master of Science)
Computer and Information Sciences
Date of Award
Committee Chair or Co-Chairs
Mohammad S. Khan, Brian T. Bennett
Edward Hall, Matthew Harrison
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.
Thesis - unrestricted
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 by the authors.