Detecting DoS Attack in Smart Home IoT Devices Using a Graph-Based Approach
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.
Paudel, Ramesh; Muncy, Timothy; and Eberle, William. 2019. Detecting DoS Attack in Smart Home IoT Devices Using a Graph-Based Approach. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. 5249-5258. https://doi.org/10.1109/BigData47090.2019.9006156 ISBN: 9781728108582