TY - GEN
T1 - IoTArgos
T2 - 38th IEEE Conference on Computer Communications, INFOCOM 2020
AU - Wan, Yinxin
AU - Xu, Kuai
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:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - The wide deployment of IoT systems in smart homes has changed the landscape of networked systems, Internet traffic, and data communications in residential broadband networks as well as the Internet at large. However, recent spates of cyber attacks and threats towards IoT systems in smart homes have revealed prevalent vulnerabilities and risks of IoT systems ranging from data link layer protocols to application services. To address the security challenges of IoT systems in smart homes, this paper introduces IoTArgos, a multi-layer security monitoring system, which collects, analyzes, and characterizes data communications of heterogeneous IoT devices via programmable home routers. More importantly, this system extracts a variety of multi-layer data communication features and develops supervised learning methods for classifying intrusion activities at system, network, and application layers. In light of the potential zero-day or unknown attacks, IoTArgos also incorporates unsupervised learning algorithms to discover unusual or suspicious behaviors towards smart home IoT systems. Our extensive experimental evaluations have demonstrated that IoTArgos is able to detect anomalous activities targeting IoT devices in smart homes with a precision of 0.9876 and a recall of 0.9763.
AB - The wide deployment of IoT systems in smart homes has changed the landscape of networked systems, Internet traffic, and data communications in residential broadband networks as well as the Internet at large. However, recent spates of cyber attacks and threats towards IoT systems in smart homes have revealed prevalent vulnerabilities and risks of IoT systems ranging from data link layer protocols to application services. To address the security challenges of IoT systems in smart homes, this paper introduces IoTArgos, a multi-layer security monitoring system, which collects, analyzes, and characterizes data communications of heterogeneous IoT devices via programmable home routers. More importantly, this system extracts a variety of multi-layer data communication features and develops supervised learning methods for classifying intrusion activities at system, network, and application layers. In light of the potential zero-day or unknown attacks, IoTArgos also incorporates unsupervised learning algorithms to discover unusual or suspicious behaviors towards smart home IoT systems. Our extensive experimental evaluations have demonstrated that IoTArgos is able to detect anomalous activities targeting IoT devices in smart homes with a precision of 0.9876 and a recall of 0.9763.
UR - http://www.scopus.com/inward/record.url?scp=85090276706&partnerID=8YFLogxK
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U2 - 10.1109/INFOCOM41043.2020.9155424
DO - 10.1109/INFOCOM41043.2020.9155424
M3 - Conference contribution
AN - SCOPUS:85090276706
T3 - Proceedings - IEEE INFOCOM
SP - 874
EP - 883
BT - INFOCOM 2020 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 July 2020 through 9 July 2020
ER -