Multidimensional behavioral profiling of internet-of-things in edge networks

Abstract

The last decade has witnessed research advances and wide deployment of Internet-of-things (IoT) in smart homes and connected industry. However, the recent spate of cyber attacks exploiting the vulnerabilities and insufficient security management of IoT devices have created serious challenges for securing IoT devices and applications. As a first step towards understanding and mitigating diverse security threats of IoT devices, this paper develops a measurement framework to automatically collect network traffic of IoT devices in edge networks, and build multidimensional behavioral profiles of these devices which characterize who, when, what, and why on the behavioral patterns of IoT devices based on continuously collected traffic data. To the best of our knowledge, this paper is the first effort to shed light on the IP-spatial, temporal, and cloud service patterns of IoT devices in edge networks, and to explore these multidimensional behavioral fingerprints for IoT device classification, anomaly traffic detection, and network security monitoring for millions of vulnerable and resource-constrained IoT devices on the Internet.