CCE Theses and Dissertations
Date of Award
2017
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Information Systems (DCIS)
Department
College of Engineering and Computing
Advisor
James D. Cannady
Committee Member
Wei Li
Committee Member
Glyn T. Gowing
Keywords
artificial intelligence, continuous layered confusion matrix, intrusion confidence level, intrusion detection, network attacks, real time detection
Abstract
For years, intrusion detection has been considered a key component of many organizations’ network defense capabilities. Although a number of approaches to intrusion detection have been tried, few have been capable of providing security personnel responsible for the protection of a network with sufficient information to make adjustments and respond to attacks in real-time. Because intrusion detection systems rarely have complete information, false negatives and false positives are extremely common, and thus valuable resources are wasted responding to irrelevant events. In order to provide better actionable information for security personnel, a mechanism for quantifying the confidence level in predictions is needed. This work presents an approach which seeks to combine a primary prediction model with a novel secondary confidence level model which provides a measurement of the confidence in a given attack prediction being made. The ability to accurately identify an attack and quantify the confidence level in the prediction could serve as the basis for a new generation of intrusion detection devices, devices that provide earlier and better alerts for administrators and allow more proactive response to events as they are occurring.
NSUWorks Citation
Jacob D. Arthur. 2017. Enhanced Prediction of Network Attacks Using Incomplete Data. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. (1020)
https://nsuworks.nova.edu/gscis_etd/1020.