Improving Reliability for Detecting Anomalies in the MQTT Network by Applying Correlation Analysis for Feature Selection Using Machine Learning Techniques

Imran,; Zuhairi, MFA; Ali, SM; Shahid, Z; Alam, MM; Su’ud, MM

Improving Reliability for Detecting Anomalies in the MQTT Network by Applying Correlation Analysis for Feature Selection Using Machine Learning Techniques

Imran, Zuhairi, MFA Ali, SM Shahid, Z Alam, MM Su’ud, MM

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Applied Sciences, 13, (11), pp. 6753

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Field	Value	Language
dc.contributor.author	Imran,
dc.contributor.author	Zuhairi, MFA
dc.contributor.author	Ali, SM
dc.contributor.author	Shahid, Z
dc.contributor.author	Alam, MM
dc.contributor.author	Su’ud, MM
dc.date.accessioned	2024-05-09T00:22:46Z
dc.date.available	2024-05-09T00:22:46Z
dc.identifier.citation	Applied Sciences, 13, (11), pp. 6753
dc.identifier.issn	2076-3417
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/178751
dc.description.abstract	Anomaly detection AD has captured a significant amount of focus from the research field in recent years with the rise of the Internet of Things IoT application Anomalies often known as outliers are defined as the discovery of anomalous occurrences or observations that differ considerably from the mainstream of the data The IoT which is described as a network of Internet based digital sensors that continuously generate massive volumes of data and use to communicate with one another theMessage Queuing Telemetry Transport MQTT protocol Brute force Denial of Service DoS Malformed Flood and Slowite attacks are the most common in theMQTT network One of the significant factors in IoT AD is the time consumed to predict an attack and take preemptive measures For instance if an attack is detected late the loss of attack is irreversible This paper investigates the time to detect an attack using machine learning approaches and proposes a novel approach that applies correlation analysis to reduce the training and testing time of these algorithms The new approach has been evaluated on Random Forest Decision Tree Na ve Bayes Multi Layer Perceptron Artificial Neural Network Logistic Regression and Gradient Boost The findings indicate that the correlation analysis is significantly beneficial in the process of feature engineering primarily to determine the most relevant features in the MQTT dataset This is to the best of our knowledge the first study on MQTTset that reduces the prediction time for DoS 0 92 95 CI 0 378 2 22 reduced to 0 77 95 CI 0 414 1 97 and for Malformed 2 92 95 CI 2 6 8 44 reduced to 0 49 95 CI 0 273 1 25
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Applied Sciences
dc.relation.isbasedon	10.3390/app13116753
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Improving Reliability for Detecting Anomalies in the MQTT Network by Applying Correlation Analysis for Feature Selection Using Machine Learning Techniques
dc.type	Journal Article
utslib.citation.volume	13
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	open_access	*
pubs.consider-herdc	true
dc.date.updated	2024-05-09T00:22:45Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	11

Abstract:

Anomaly detection AD has captured a significant amount of focus from the research field in recent years with the rise of the Internet of Things IoT application Anomalies often known as outliers are defined as the discovery of anomalous occurrences or observations that differ considerably from the mainstream of the data The IoT which is described as a network of Internet based digital sensors that continuously generate massive volumes of data and use to communicate with one another theMessage Queuing Telemetry Transport MQTT protocol Brute force Denial of Service DoS Malformed Flood and Slowite attacks are the most common in theMQTT network One of the significant factors in IoT AD is the time consumed to predict an attack and take preemptive measures For instance if an attack is detected late the loss of attack is irreversible This paper investigates the time to detect an attack using machine learning approaches and proposes a novel approach that applies correlation analysis to reduce the training and testing time of these algorithms The new approach has been evaluated on Random Forest Decision Tree Na ve Bayes Multi Layer Perceptron Artificial Neural Network Logistic Regression and Gradient Boost The findings indicate that the correlation analysis is significantly beneficial in the process of feature engineering primarily to determine the most relevant features in the MQTT dataset This is to the best of our knowledge the first study on MQTTset that reduces the prediction time for DoS 0 92 95 CI 0 378 2 22 reduced to 0 77 95 CI 0 414 1 97 and for Malformed 2 92 95 CI 2 6 8 44 reduced to 0 49 95 CI 0 273 1 25

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http://hdl.handle.net/10453/178751