| Title | Detecting DoS Attack in Smart Home IoT Devices Using a Graph-Based Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Paudel, Ramesh, Muncy, Timothy, Eberle, William |
| Conference Name | 2019 IEEE International Conference on Big Data (Big Data) |
| Date Published | dec |
| Keywords | anomaly detection, Attack Graphs, attack signatures, composability, computer network security, current security solutions, cyber-attacks, data mining, denial-of-service attacks, DoS attack, DoS Attack Detection, GODIT, graph data, graph mining, graph theory, graph-based outlier detection in Internet of Things, graph-stream anomaly detection approaches, Internet of Things, Intrusion detection, IoT security, IoT-equipped smart home show, IP networks, learning (artificial intelligence), machine learning approaches, packet inspection, Predictive Metrics, pubcrawl, real-time graph stream, Real-time Systems, Resiliency, smart home, smart home IoT devices, smart home IoT traffic, Smart homes |
| Abstract | 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. |
| DOI | 10.1109/BigData47090.2019.9006156 |
| Citation Key | paudel_detecting_2019 |
Detecting DoS Attack in Smart Home IoT Devices Using a Graph-Based Approach