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- W2898967461 abstract "The Internet of Things, or IoT, is continuously growing with more devices beingconnected every day, adding new features and functionality to our personal and homedevices by connecting them to the Internet. However, with the increase of devicesand components, new security threats arise in previously offline systems that usedto be immune to network attacks. This increase calls for better security options thatcan ensure protection of the data flowing in IoT networks by detecting and mitigatingnew threats. To contribute to the mentioned area, the goal of this thesis was todevelop and evaluate a modified version of Snort, a widely used intrusion detectionsystem. The idea was to improve the efficiency of computationally expensive patternmatching by extending Snort to use a graphical processing unit for such work. TheSnort modification was tested by comparing it to that of an unmodified version ofSnort in a closed environment with simulated network traffic. The tests were runon a single-board-computer to simulate the IoT context. The results show thatthe new functionality yields a speedup of 1.3 when analyzing captured traffic, athroughput increase of a factor of two when inspecting live traffic, and slightly lessenergy consumption, all when comparing to original Snort. With these results, itseems plausible to use the IoT devices as a means of strengthening their own securityand protect them from network attacks." @default.
- W2898967461 created "2018-11-09" @default.
- W2898967461 creator A5022167587 @default.
- W2898967461 creator A5071796721 @default.
- W2898967461 date "2018-01-01" @default.
- W2898967461 modified "2023-09-24" @default.
- W2898967461 title "Improving Intrusion Detection for IoT Networks - A Snort GPGPU Modification Using OpenCL" @default.
- W2898967461 hasPublicationYear "2018" @default.
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