Spline-based intrusion detection for VANET utilizing knot flow classification

Abstract

Intrusion detection systems (IDSs) are an integral component for the identification and mitigation of attacks on computing systems. Of these systems, vehicular ad hoc networks (VANETs) are particularly difficult to protect due to the dynamic nature of their clients and the volume of information passed between them and their respective infrastructure. To meet these requirements, a spline-based intrusion detection system has been pioneered as a prospective solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows robust intrusion detection to occur.