The use of the Internet of Things (IoT) devices has surged in recent years. However, due to the lack of substantial security, IoT devices are vulnerable to cyber-attacks like Denial-of-Service (DoS) attacks. Most of the current security solutions are either computationally expensive or unscalable as they require known attack signatures or full packet inspection. In this paper, we introduce a novel Graph-based Outlier Detection in Internet of Things (GODIT) approach that (i) represents smart home IoT traffic as a real-time graph stream, (ii) efficiently processes graph data, and (iii) detects DoS attack in real-time. The experimental results on real-world data collected from IoT-equipped smart home show that GODIT is more effective than the traditional machine learning approaches, and is able to outperform current graph-stream anomaly detection approaches.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etsu-works-11367 |
| Date | 01 December 2019 |
| Creators | Paudel, Ramesh, Muncy, Timothy, Eberle, William |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Detected Language | English |
| Type | text |
| Source | ETSU Faculty Works |
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