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Automated IoT device identification based on full packet information using real-time network traffic.

Yousefnezhad, N., Malhi, A. and Främling, K., 2021. Automated IoT device identification based on full packet information using real-time network traffic. Sensors, 21 (8), 2660.

Full text available as:

sensors-21-02660-v2.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.3390/s21082660


In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness.

Item Type:Article
Additional Information:This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020.
Uncontrolled Keywords:device identification; IoT Security; device profiling; real-time traffic; machine learning
Group:Faculty of Science & Technology
ID Code:35408
Deposited By: Symplectic RT2
Deposited On:19 Apr 2021 10:26
Last Modified:14 Mar 2022 14:27


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