Date of Graduation
Statler College of Engineering and Mineral Resources
Lane Department of Computer Science and Electrical Engineering
Roy S. Nutter
Katerina D. Goseva-Popstojanova
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.
Armendariz Jr., Luis C., "Non-intrusive anomaly detection for encrypted networks" (2014). Graduate Theses, Dissertations, and Problem Reports. 111.