XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection
In the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and comp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Telecom |
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| Online Access: | https://www.mdpi.com/2673-4001/3/1/3 |
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| author | Jabed Al Faysal Sk Tahmid Mostafa Jannatul Sultana Tamanna Khondoker Mirazul Mumenin Md. Mashrur Arifin Md. Abdul Awal Atanu Shome Sheikh Shanawaz Mostafa |
| author_facet | Jabed Al Faysal Sk Tahmid Mostafa Jannatul Sultana Tamanna Khondoker Mirazul Mumenin Md. Mashrur Arifin Md. Abdul Awal Atanu Shome Sheikh Shanawaz Mostafa |
| author_sort | Jabed Al Faysal |
| collection | DOAJ |
| description | In the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and computing resources necessary for adequate security operations. As a result, IoT devices are affected by a variety of attacks. A single attack on network systems or devices can lead to significant damages in data security and privacy. However, machine-learning techniques can be applied to detect IoT attacks. In this paper, a hybrid machine learning scheme called XGB-RF is proposed for detecting intrusion attacks. The proposed hybrid method was applied to the N-BaIoT dataset containing hazardous botnet attacks. Random forest (RF) was used for the feature selection and eXtreme Gradient Boosting (XGB) classifier was used to detect different types of attacks on IoT environments. The performance of the proposed XGB-RF scheme is evaluated based on several evaluation metrics and demonstrates that the model successfully detects 99.94% of the attacks. After comparing it with state-of-the-art algorithms, our proposed model has achieved better performance for every metric. As the proposed scheme is capable of detecting botnet attacks effectively, it can significantly contribute to reducing the security concerns associated with IoT systems. |
| first_indexed | 2024-03-09T12:24:15Z |
| format | Article |
| id | doaj.art-efcca67ea96240db847d4cf993eb753e |
| institution | Directory Open Access Journal |
| issn | 2673-4001 |
| language | English |
| last_indexed | 2024-03-09T12:24:15Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Telecom |
| spelling | doaj.art-efcca67ea96240db847d4cf993eb753e2023-11-30T22:37:29ZengMDPI AGTelecom2673-40012022-01-0131526910.3390/telecom3010003XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion DetectionJabed Al Faysal0Sk Tahmid Mostafa1Jannatul Sultana Tamanna2Khondoker Mirazul Mumenin3Md. Mashrur Arifin4Md. Abdul Awal5Atanu Shome6Sheikh Shanawaz Mostafa7Computer Science and Engineering Discipline (CSE), Khulna University (KU), Khulna 9208, BangladeshElectronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, BangladeshElectronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, BangladeshElectronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, BangladeshElectronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, BangladeshElectronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, BangladeshComputer Science and Engineering Discipline (CSE), Khulna University (KU), Khulna 9208, BangladeshITI—Interactive Technologies Institute, LARSyS, Laboratory of Robotics and Systems in Engineering and Science, M-ITI, ARDITI, 9000 Funchal, PortugalIn the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and computing resources necessary for adequate security operations. As a result, IoT devices are affected by a variety of attacks. A single attack on network systems or devices can lead to significant damages in data security and privacy. However, machine-learning techniques can be applied to detect IoT attacks. In this paper, a hybrid machine learning scheme called XGB-RF is proposed for detecting intrusion attacks. The proposed hybrid method was applied to the N-BaIoT dataset containing hazardous botnet attacks. Random forest (RF) was used for the feature selection and eXtreme Gradient Boosting (XGB) classifier was used to detect different types of attacks on IoT environments. The performance of the proposed XGB-RF scheme is evaluated based on several evaluation metrics and demonstrates that the model successfully detects 99.94% of the attacks. After comparing it with state-of-the-art algorithms, our proposed model has achieved better performance for every metric. As the proposed scheme is capable of detecting botnet attacks effectively, it can significantly contribute to reducing the security concerns associated with IoT systems.https://www.mdpi.com/2673-4001/3/1/3IoT securitybotnet detectionrandom forestXGBfeature selectionMirai |
| spellingShingle | Jabed Al Faysal Sk Tahmid Mostafa Jannatul Sultana Tamanna Khondoker Mirazul Mumenin Md. Mashrur Arifin Md. Abdul Awal Atanu Shome Sheikh Shanawaz Mostafa XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection Telecom IoT security botnet detection random forest XGB feature selection Mirai |
| title | XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection |
| title_full | XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection |
| title_fullStr | XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection |
| title_full_unstemmed | XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection |
| title_short | XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection |
| title_sort | xgb rf a hybrid machine learning approach for iot intrusion detection |
| topic | IoT security botnet detection random forest XGB feature selection Mirai |
| url | https://www.mdpi.com/2673-4001/3/1/3 |
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