<p>This thesis investigates methods for implementing an intrusion detection system (IDS) in a high-speed backbone network. The work presented in this report is run in cooperation with Kripos and Uninett. The popular IDS software, Snort, is deployed and tested in Uninett's backbone network. In addition, the monitoring API (MAPI) is considered as a possible IDS implementation in the same environment. The experiments conducted in this report make use of the programmable DAG card, which is a passive monitoring card deployed on several monitoring sensors in Uninett's backbone. As a limitation of the workload, this report only focuses on the detection of botnets. Botnets are networks consisting of infected computers, and are considered to be a significant threat on the Internet as of today. A total of seven experiments using Snort are presented. These experiments test 1) the impact the number of rules have on Snort, 2) the importance of good configuration, 3)the importance of using well written rules, 4) Snort's ability to run in an environment with minimum external traffic, 5) the impact the size of the processed packets have, 6) the impact the TCP protocol has on packet processing and 7) Snort's ability to run as a botnet detection system for a longer period of time. Based on the results from these experiments, it is concluded that Snort is able to run as a botnet detection system in a high-speed network. This report also discusses some strategies for handling high-speed network data and some future aspects. In addition, ideas for further work and research are given in the end of the report.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:ntnu-8785 |
| Date | January 2007 |
| Creators | Riegel, Martin, Walsø, Claes Lyth |
| Publisher | Norwegian University of Science and Technology, Department of Telematics, Norges Teknisk-Naturvitenskaplige Universitet, Institutt for datateknikk og informasjonsvitenskap, Institutt for telematikk |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, text |
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