The goal of this research is to improve an IDS so that the percentage of true positives is high, an organisation can cut time and cost and use its resources in a more optimal way. This research goal was to prove that the precision of an intrusion detection system (IDS), in terms of producing lower rate of false positives or higher rate of true alerts, can be achieved by parsing indicators of compromise (IOC) to gather information, that combined with system-specific knowledge will be a solid base for manual fine-tuning of IDS-rules. The methodology used is Design Science Research Methodology (DSRM) because it is used for research that aims to answer an existing problem with a new or improved solution. A part of that solution is a proposed process for tuning of an arbitrary intrusion detection system. The implemented and formalized process Tuned Intrusion Detection System (TIDS) has been designed during this research work, aiding us in presenting and performing validation tests in a structured and robust way. The testbed consisted of a Windows 10 operating system and a NIDS implementation of Snort as an IDS. The work was experimental, evaluated and improved regarding IDS rules and tools over several iterations. With the use of recorded data traffic from the public dataset CTU-13, the difference between the use of tuned versus un-tuned rules in an IDS was presented in terms of precision of the alerts created by the IDS. Our contributions were that the concept holds; the precision can be improved by adding custom rules based on known parameters in the network and features of the network traffic and disabling rules that were out of scope. The second contribution is the TIDS process, as designed during the thesis work, serving us well during the process.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-69997 |
| Date | January 2018 |
| Creators | Lejonqvist, Gisela, Larsson, Oskar |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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