Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS
Smart homes are becoming increasingly popular worldwide, and they are mainly based on Internet of Things (IoT) technologies to enable their functionality. However, because IoT devices have limited computing power and resources, implementing strong security measures is difficult, making the use of in...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10439191/ |
| _version_ | 1797296325807570944 |
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| author | Rana Alasmari Areej Abdullah Alhogail |
| author_facet | Rana Alasmari Areej Abdullah Alhogail |
| author_sort | Rana Alasmari |
| collection | DOAJ |
| description | Smart homes are becoming increasingly popular worldwide, and they are mainly based on Internet of Things (IoT) technologies to enable their functionality. However, because IoT devices have limited computing power and resources, implementing strong security measures is difficult, making the use of intrusion detection systems (IDS) an appropriate option. In this study, we propose an optimized model with high performance for intrusion detection in Message Queue Telemetry Transport protocol (MQTT)-based IoT networks for smart homes. This is done by studying 22 Machine Learning (ML) algorithms based on an extended two-stage evaluation approach that includes several aspects for optimizing and validating the performance to find the ideal model. Based on the empirical evaluation, the Generalized Linear Model (GLM) classifier with the random over-sampling technique produced the best detection performance with 100% accuracy and an f-score of 100%, outperforming previous studies. This study also investigated the influence of automatic feature engineering techniques on the performance of algorithms. With the automatic feature engineering technique, the performance increased by up to 38.9%, and the time required to classify the attacks decreased by up to 67.7%. This shows that automatic feature engineering can improve performance and reduce detection time. |
| first_indexed | 2024-03-07T22:03:00Z |
| format | Article |
| id | doaj.art-2a49cdc07eb74cf584e5ab5679b29c96 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T22:03:00Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2a49cdc07eb74cf584e5ab5679b29c962024-02-24T00:01:00ZengIEEEIEEE Access2169-35362024-01-0112259932600410.1109/ACCESS.2024.336711310439191Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDSRana Alasmari0https://orcid.org/0009-0008-8003-4214Areej Abdullah Alhogail1https://orcid.org/0000-0003-0573-0427College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, STCs Artificial Intelligence Chair, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaSmart homes are becoming increasingly popular worldwide, and they are mainly based on Internet of Things (IoT) technologies to enable their functionality. However, because IoT devices have limited computing power and resources, implementing strong security measures is difficult, making the use of intrusion detection systems (IDS) an appropriate option. In this study, we propose an optimized model with high performance for intrusion detection in Message Queue Telemetry Transport protocol (MQTT)-based IoT networks for smart homes. This is done by studying 22 Machine Learning (ML) algorithms based on an extended two-stage evaluation approach that includes several aspects for optimizing and validating the performance to find the ideal model. Based on the empirical evaluation, the Generalized Linear Model (GLM) classifier with the random over-sampling technique produced the best detection performance with 100% accuracy and an f-score of 100%, outperforming previous studies. This study also investigated the influence of automatic feature engineering techniques on the performance of algorithms. With the automatic feature engineering technique, the performance increased by up to 38.9%, and the time required to classify the attacks decreased by up to 67.7%. This shows that automatic feature engineering can improve performance and reduce detection time.https://ieeexplore.ieee.org/document/10439191/IoT securityintrusion detectionmachine learningMQTTsmart homesautomatic feature engineering |
| spellingShingle | Rana Alasmari Areej Abdullah Alhogail Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS IEEE Access IoT security intrusion detection machine learning MQTT smart homes automatic feature engineering |
| title | Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS |
| title_full | Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS |
| title_fullStr | Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS |
| title_full_unstemmed | Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS |
| title_short | Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS |
| title_sort | protecting smart home iot devices from mqtt attacks an empirical study of ml based ids |
| topic | IoT security intrusion detection machine learning MQTT smart homes automatic feature engineering |
| url | https://ieeexplore.ieee.org/document/10439191/ |
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