Accurate Detection of IoT Sensor Behaviors in Legitimate, Faulty and Compromised Scenarios

Sood, K; Nosouhi, MR; Kumar, N; Gaddam, A; Feng, B; Yu, S

Accurate Detection of IoT Sensor Behaviors in Legitimate, Faulty and Compromised Scenarios

Sood, K Nosouhi, MR Kumar, N Gaddam, A Feng, B Yu, S

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Dependable and Secure Computing, 2022, 20, (1), pp. 288-300
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Sood, K
dc.contributor.author	Nosouhi, MR
dc.contributor.author	Kumar, N
dc.contributor.author	Gaddam, A
dc.contributor.author	Feng, B
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2023-03-20T00:02:28Z
dc.date.available	2023-03-20T00:02:28Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Dependable and Secure Computing, 2022, 20, (1), pp. 288-300
dc.identifier.issn	1545-5971
dc.identifier.issn	1941-0018
dc.identifier.uri	http://hdl.handle.net/10453/167642
dc.description.abstract	In smart farming sector, Internet of Things (IoT) based smart sensing systems are vulnerable to failure, malfunction, and malicious attacks. Also, sensors are deployed often in an alien and harsh environment. Here, the conditions are not well supportive which either causes the sensor to fail prematurely or gives unusual and erroneous readings, known as outliers. This effects the smart networks performance and decision-making ability in many ways. Therefore, it is important to accurately detect the IoT sensor behaviour in legitimate, faulty, and compromised or attack scenarios. To distinguish the sensor behaviour in different scenarios we have proposed a feasible approach using spatial correlation theory which is validated using Morans I index tool. We have used Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) models to test our approach. For real-time anomaly detection we have used an edge computing technology. We have compared the proposed approach, using Forest Fire real dataset, with the three existing recent works. Our results are promising in terms of accurate detection of IoT sensor behaviours in real-time. This will assist the precision farming industry in making better decisions to securely manage IoT field network, increase productivity, and improves operational efficiency.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation	http://purl.org/au-research/grants/arc/LP190100676
dc.relation.ispartof	IEEE Transactions on Dependable and Secure Computing
dc.relation.isbasedon	10.1109/TDSC.2021.3131991
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0804 Data Format, 0805 Distributed Computing
dc.subject.classification	Strategic, Defence & Security Studies
dc.title	Accurate Detection of IoT Sensor Behaviors in Legitimate, Faulty and Compromised Scenarios
dc.type	Journal Article
utslib.citation.volume	20
utslib.for	0803 Computer Software
utslib.for	0804 Data Format
utslib.for	0805 Distributed Computing
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	*
pubs.consider-herdc	false
dc.date.updated	2023-03-20T00:02:27Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	1

Abstract:

In smart farming sector, Internet of Things (IoT) based smart sensing systems are vulnerable to failure, malfunction, and malicious attacks. Also, sensors are deployed often in an alien and harsh environment. Here, the conditions are not well supportive which either causes the sensor to fail prematurely or gives unusual and erroneous readings, known as outliers. This effects the smart networks performance and decision-making ability in many ways. Therefore, it is important to accurately detect the IoT sensor behaviour in legitimate, faulty, and compromised or attack scenarios. To distinguish the sensor behaviour in different scenarios we have proposed a feasible approach using spatial correlation theory which is validated using Morans I index tool. We have used Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) models to test our approach. For real-time anomaly detection we have used an edge computing technology. We have compared the proposed approach, using Forest Fire real dataset, with the three existing recent works. Our results are promising in terms of accurate detection of IoT sensor behaviours in real-time. This will assist the precision farming industry in making better decisions to securely manage IoT field network, increase productivity, and improves operational efficiency.

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http://hdl.handle.net/10453/167642