TY - GEN
T1 - Multidimensional behavioral profiling of internet-of-things in edge networks
AU - Xu, Kuai
AU - Wan, Yinxin
AU - Xue, Guoliang
AU - Wang, Feng
N1 - Funding Information:
This research was supported in part by NSF grants 1816995, 1717197, and 1704092. The information reported here does not reflect the position or the policy of the funding agency.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/6/24
Y1 - 2019/6/24
N2 - 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.
AB - 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.
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U2 - 10.1145/3326285.3329072
DO - 10.1145/3326285.3329072
M3 - Conference contribution
AN - SCOPUS:85069216170
T3 - Proceedings of the International Symposium on Quality of Service, IWQoS 2019
BT - Proceedings of the International Symposium on Quality of Service, IWQoS 2019
PB - Association for Computing Machinery, Inc
T2 - 2019 International Symposium on Quality of Service, IWQoS 2019
Y2 - 24 June 2019 through 25 June 2019
ER -