TY - GEN
T1 - IoT Device Type Identification Using Hybrid Deep Learning Approach for Increased IoT Security
AU - Bao, Jiaqi
AU - Hamdaoui, Bechir
AU - Wong, Weng Keen
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - IoT networks can be viewed as collections of Internet-enabled physical devices and objects, embedded with sensor, actuator, computation, storage and communication components, that are capable of connecting and exchanging data to one another. In recent years, organizations have allowed more and more IoT devices to be connected to their networks, thereby increasing their risks of and exposure to security vulnerabilities and threats. Therefore, it is important for such organizations to be able to identify which devices are connected to their network and which ones are legitimate and pose no risk. Leveraging network traffic to identify devices through supervised learning has recently been gaining popularity, where feature information is first extracted by intercepting device traffic and then exploited to provide device classification. The main limitation of prior works is that they can only identify previously seen types of devices, and any newly added device types are treated as abnormal types. In the real world, hundreds of millions of new IoT devices are produced each year, and the lack of a large amount of training data makes a system based solely on supervised learning unrealistic. In this paper, we propose a hybrid supervised and unsupervised learning method that enables secondary classification of unseen device types. Our technique combines deep neural networks with clustering to enable both seen and unseen device classification, and employs autoencoder technique to reduce dimensionality of datasets, thereby providing a good balance between classification accuracy and overhead.
AB - IoT networks can be viewed as collections of Internet-enabled physical devices and objects, embedded with sensor, actuator, computation, storage and communication components, that are capable of connecting and exchanging data to one another. In recent years, organizations have allowed more and more IoT devices to be connected to their networks, thereby increasing their risks of and exposure to security vulnerabilities and threats. Therefore, it is important for such organizations to be able to identify which devices are connected to their network and which ones are legitimate and pose no risk. Leveraging network traffic to identify devices through supervised learning has recently been gaining popularity, where feature information is first extracted by intercepting device traffic and then exploited to provide device classification. The main limitation of prior works is that they can only identify previously seen types of devices, and any newly added device types are treated as abnormal types. In the real world, hundreds of millions of new IoT devices are produced each year, and the lack of a large amount of training data makes a system based solely on supervised learning unrealistic. In this paper, we propose a hybrid supervised and unsupervised learning method that enables secondary classification of unseen device types. Our technique combines deep neural networks with clustering to enable both seen and unseen device classification, and employs autoencoder technique to reduce dimensionality of datasets, thereby providing a good balance between classification accuracy and overhead.
KW - IoT device type classification
KW - IoT security
KW - deep learning
KW - network traffic monitoring
UR - http://www.scopus.com/inward/record.url?scp=85089693217&partnerID=8YFLogxK
U2 - 10.1109/IWCMC48107.2020.9148110
DO - 10.1109/IWCMC48107.2020.9148110
M3 - Conference contribution
AN - SCOPUS:85089693217
T3 - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
SP - 565
EP - 570
BT - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
Y2 - 15 June 2020 through 19 June 2020
ER -