Semester
Spring
Date of Graduation
2014
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Lane Department of Computer Science and Electrical Engineering
Committee Chair
Roy S. Nutter
Committee Co-Chair
Katerina D. Goseva-Popstojanova
Committee Member
Afzel Noore.
Abstract
The use of encryption is steadily increasing. Packet payloads that are encrypted are becoming increasingly difficult to analyze using IDSs. This investigation uses a new non-intrusive IDS approach to detect network intrusions using a K-Means clustering methodology. It was found that this approach was able to detect many intrusions for these datasets while maintaining the encrypted confidentiality of packet information. This work utilized the KDD '99 and NSL-KDD evaluation datasets for testing.
Recommended Citation
Armendariz Jr., Luis C., "Non-intrusive anomaly detection for encrypted networks" (2014). Graduate Theses, Dissertations, and Problem Reports. 111.
https://researchrepository.wvu.edu/etd/111