Towards Spoofing Resistant Next Generation IoT Networks

Nosouhi, MR; Sood, K; Grobler, M; Doss, R

Towards Spoofing Resistant Next Generation IoT Networks

Nosouhi, MR Sood, K Grobler, M Doss, R

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2022, 17, pp. 1669-1683
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Nosouhi, MR
dc.contributor.author	Sood, K
dc.contributor.author	Grobler, M
dc.contributor.author	Doss, R
dc.date.accessioned	2023-03-02T00:04:33Z
dc.date.available	2023-03-02T00:04:33Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2022, 17, pp. 1669-1683
dc.identifier.issn	1556-6013
dc.identifier.issn	1556-6021
dc.identifier.uri	http://hdl.handle.net/10453/166604
dc.description.abstract	The potential vulnerability to wireless spoofing attacks is still a critical concern for Next Generation Internet of Things (NGIoT) networks which may result in catastrophic consequences in mission-critical applications. Conventional solutions may impose additional signal processing, protocol, and latency overheads which are inappropriate for NGIoT networks designed to provide high-speed and low-latency connections for a large number of resource-constrained IoT devices. In this paper, we utilize the uniqueness of beam pattern features in mmWave-enabled devices and propose a scalable security mechanism for the detection of wireless spoofing attacks in NGIoT networks. This uniqueness is proven to exist due to the non-ideal manufacturing of antenna arrays used in mmWave-enabled devices. In our approach, when legitimate mmWave-enabled IoT devices enrol into the network, their unique beam features are learned by a learning model developed at the network server. Then, during data transmission, network base stations (gNBs)/Access Points (APs) measure the beam features from the received RF signals and send them to the network server for the detection of anomalies. We develop our learning model based on Deep Autoencoders (DAEs) that are an effective tool for anomaly detection. Fortunately, the beam feature extraction can be performed using the beam searching mechanism that is already provided in mmWave standards (5G-NR and IEEE 802.11ad). Thus, feature extraction does not introduce any signal processing overheads to the system. Moreover, the proposed mechanism imposes zero computation/communication overhead to the resource - constrained IoT nodes. In our experiments, we reached 98.6% accuracy in the detection of illegitimate devices which confirms the effectiveness of the proposed approach.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security
dc.relation.isbasedon	10.1109/TIFS.2022.3170276
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Strategic, Defence & Security Studies
dc.title	Towards Spoofing Resistant Next Generation IoT Networks
dc.type	Journal Article
utslib.citation.volume	17
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-02T00:04:08Z
pubs.publication-status	Published
pubs.volume	17

Abstract:

The potential vulnerability to wireless spoofing attacks is still a critical concern for Next Generation Internet of Things (NGIoT) networks which may result in catastrophic consequences in mission-critical applications. Conventional solutions may impose additional signal processing, protocol, and latency overheads which are inappropriate for NGIoT networks designed to provide high-speed and low-latency connections for a large number of resource-constrained IoT devices. In this paper, we utilize the uniqueness of beam pattern features in mmWave-enabled devices and propose a scalable security mechanism for the detection of wireless spoofing attacks in NGIoT networks. This uniqueness is proven to exist due to the non-ideal manufacturing of antenna arrays used in mmWave-enabled devices. In our approach, when legitimate mmWave-enabled IoT devices enrol into the network, their unique beam features are learned by a learning model developed at the network server. Then, during data transmission, network base stations (gNBs)/Access Points (APs) measure the beam features from the received RF signals and send them to the network server for the detection of anomalies. We develop our learning model based on Deep Autoencoders (DAEs) that are an effective tool for anomaly detection. Fortunately, the beam feature extraction can be performed using the beam searching mechanism that is already provided in mmWave standards (5G-NR and IEEE 802.11ad). Thus, feature extraction does not introduce any signal processing overheads to the system. Moreover, the proposed mechanism imposes zero computation/communication overhead to the resource - constrained IoT nodes. In our experiments, we reached 98.6% accuracy in the detection of illegitimate devices which confirms the effectiveness of the proposed approach.

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http://hdl.handle.net/10453/166604