Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments
The explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environmen...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/6/1782 |
| _version_ | 1797239376411885568 |
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| author | Abdulelah Al Hanif Mohammad Ilyas |
| author_facet | Abdulelah Al Hanif Mohammad Ilyas |
| author_sort | Abdulelah Al Hanif |
| collection | DOAJ |
| description | The explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environments. MQTT enables fast and lightweight communication between IoT devices to facilitate data exchange, but this flexibility also exposes MQTT to significant security vulnerabilities and challenges that demand highly robust security. This paper aims to enhance the detection efficiency of an MQTT traffic intrusion detection system (IDS). Our proposed approach includes the development of a binary balanced MQTT dataset with an effective feature engineering and machine learning framework to enhance the security of MQTT traffic. Our feature selection analysis and comparison demonstrates that selecting a 10-feature model provides the highest effectiveness, as it shows significant advantages in terms of constant accuracy and superior training and testing times across all models. The results of this study show that the framework has the capability to enhance the efficiency of an IDS for MQTT traffic, with more than 96% accuracy, precision, recall, F1-score, and ROC, and it outperformed the most recent study that used the same dataset. |
| first_indexed | 2024-04-24T17:50:33Z |
| format | Article |
| id | doaj.art-70a00bf17ff249eca18c6697aca1d911 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:50:33Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-70a00bf17ff249eca18c6697aca1d9112024-03-27T14:03:46ZengMDPI AGSensors1424-82202024-03-01246178210.3390/s24061782Effective Feature Engineering Framework for Securing MQTT Protocol in IoT EnvironmentsAbdulelah Al Hanif0Mohammad Ilyas1Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USADepartment of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USAThe explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environments. MQTT enables fast and lightweight communication between IoT devices to facilitate data exchange, but this flexibility also exposes MQTT to significant security vulnerabilities and challenges that demand highly robust security. This paper aims to enhance the detection efficiency of an MQTT traffic intrusion detection system (IDS). Our proposed approach includes the development of a binary balanced MQTT dataset with an effective feature engineering and machine learning framework to enhance the security of MQTT traffic. Our feature selection analysis and comparison demonstrates that selecting a 10-feature model provides the highest effectiveness, as it shows significant advantages in terms of constant accuracy and superior training and testing times across all models. The results of this study show that the framework has the capability to enhance the efficiency of an IDS for MQTT traffic, with more than 96% accuracy, precision, recall, F1-score, and ROC, and it outperformed the most recent study that used the same dataset.https://www.mdpi.com/1424-8220/24/6/1782Message Queuing Telemetry TransportInternet of thingssecuritymachine learningfeature selection |
| spellingShingle | Abdulelah Al Hanif Mohammad Ilyas Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments Sensors Message Queuing Telemetry Transport Internet of things security machine learning feature selection |
| title | Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments |
| title_full | Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments |
| title_fullStr | Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments |
| title_full_unstemmed | Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments |
| title_short | Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments |
| title_sort | effective feature engineering framework for securing mqtt protocol in iot environments |
| topic | Message Queuing Telemetry Transport Internet of things security machine learning feature selection |
| url | https://www.mdpi.com/1424-8220/24/6/1782 |
| work_keys_str_mv | AT abdulelahalhanif effectivefeatureengineeringframeworkforsecuringmqttprotocoliniotenvironments AT mohammadilyas effectivefeatureengineeringframeworkforsecuringmqttprotocoliniotenvironments |