IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT

By Markus Miettinen, Samuel Marchal, Ibbad Hafeez, Tommaso Frassetto, N. Asokan, Ahmad-Reza Sadeghi, Sasu Tarkoma. IEEE International Conference on Distributed Computing Systems, 2016

719 citations recorded by the source corpus.

With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IoT Sentinel, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IoT Sentinel is effective in identifying device types and has minimal performance overhead.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3657697414ad0329cdac7048ce8f16f4dd1f0415

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Citations in the corpus

Peek-a-boo: i see your smart home activities, even encrypted! (2018, background): “Many studies have investigated the device type identification problem, where it has been sometimes proposed for both attacking [8, 18, 28, 35, 36] and improving the security of smart home platforms [9, 31, 32].”
DÏoT: A Federated Self-learning Anomaly Detection System for IoT (2018, methodology): “identifying either the device model [13] or the specific hardware / software configuration of a device [14]–[17] by training classification models with labeled data from specific known device types.”
Machine Learning DDoS Detection for Consumer Internet of Things Devices (2018, background): “Miettinen et al. further show how machine learning techniques can leverage the unique patterns of IoT network trafﬁc for similar tasks, such as device identiﬁcation [14].”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “Miettinen et al. [152] presented an IoT SENTINEL model in which the classiﬁer categorizes the IoT devices using RF algorithm to secure it from any unprotected device connection and avoid damage.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “A system called ‘IoT SENTINEL’, which is based on the RF classification algorithm, was proposed in [258] to recognize the types of devices connected to an IoT system automatically and execute an action to restrain any of vulnerable connections accordingly to reduce damage that may be caused by compr”