Thesis
Developing a methodology for determining the effectiveness of security monitoring tools within a network
Washington State University
Master of Science (MS), Washington State University
2018
Handle:
https://hdl.handle.net/2376/100811
Abstract
In 2015 a cyber attack targeted the Ukraine power grid causing a large scale power outage and exposing the possibility of similar events occurring around the world. This event demonstrated the long held concerns about such threats and the need for improved technologies to detect future malicious attacks. This thesis proposes a continuous network monitoring platform to enable the detection of sophisticated attacks within power system networks and a methodology to quantify the completeness of various monitoring technologies. Security of a network is determined based on certain simulated attack scenarios and the visibility that the monitoring technologies offer on each node in the network. The monitoring platform was developed using existing open-source technologies and validated with the Smart City Testbed at Washington State University. The platform gathers various security data (e.g., netflows, event logs, intrusion reports) from multiple sources inside the network such as the workstations, digital protection relays, and networks switches. The collected data is analyzed against a network models of the systems and quantitative scores are generated for each node based on how effective they are at monitoring for certain attacks. The resulting approach is validated against various simulated attacks within the testbed.
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Details
- Title
- Developing a methodology for determining the effectiveness of security monitoring tools within a network
- Creators
- Armin Rahimi
- Contributors
- Adam Hahn (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525006201842
- Language
- English
- Resource Type
- Thesis