A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments
The Internet of Things (IoT) has gained significant importance due to its applicability in diverse environments. Another reason for the influence of the IoT is its use of a flexible and scalable framework. The extensive and diversified use of the IoT in the past few years has attracted cyber-crimina...
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2021
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oai:doaj.org-article:e69a2d2cf64141fb848fb57c6349d7122021-11-11T15:18:25ZA Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments10.3390/app1121102682076-3417https://doaj.org/article/e69a2d2cf64141fb848fb57c6349d7122021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10268https://doaj.org/toc/2076-3417The Internet of Things (IoT) has gained significant importance due to its applicability in diverse environments. Another reason for the influence of the IoT is its use of a flexible and scalable framework. The extensive and diversified use of the IoT in the past few years has attracted cyber-criminals. They exploit the vulnerabilities of the open-source IoT framework due to the absentia of robust and standard security protocols, hence discouraging existing and potential stakeholders. The authors propose a binary classifier approach developed from a machine learning ensemble method to filter and dump malicious traffic to prevent malicious actors from accessing the IoT network and its peripherals. The gradient boosting machine (GBM) ensemble approach is used to train the binary classifier using pre-processed recorded data packets to detect the anomaly and prevent the IoT networks from zero-day attacks. The positive class performance metrics of the model resulted in an accuracy of 98.27%, a precision of 96.40%, and a recall of 95.70%. The simulation results prove the effectiveness of the proposed model against cyber threats, thus making it suitable for critical applications for the IoT.Parag VermaAnkur DumkaRajesh SinghAlaknanda AshokAnita GehlotPraveen Kumar MalikGurjot Singh GabaMustapha HedabouMDPI AGarticlemachine learningintrusion detection systemnetwork anomaly detectionInternet of Thingsgradient boosting machine (GBM)TechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10268, p 10268 (2021) |
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DOAJ |
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DOAJ |
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EN |
| topic |
machine learning intrusion detection system network anomaly detection Internet of Things gradient boosting machine (GBM) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
| spellingShingle |
machine learning intrusion detection system network anomaly detection Internet of Things gradient boosting machine (GBM) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Parag Verma Ankur Dumka Rajesh Singh Alaknanda Ashok Anita Gehlot Praveen Kumar Malik Gurjot Singh Gaba Mustapha Hedabou A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments |
| description |
The Internet of Things (IoT) has gained significant importance due to its applicability in diverse environments. Another reason for the influence of the IoT is its use of a flexible and scalable framework. The extensive and diversified use of the IoT in the past few years has attracted cyber-criminals. They exploit the vulnerabilities of the open-source IoT framework due to the absentia of robust and standard security protocols, hence discouraging existing and potential stakeholders. The authors propose a binary classifier approach developed from a machine learning ensemble method to filter and dump malicious traffic to prevent malicious actors from accessing the IoT network and its peripherals. The gradient boosting machine (GBM) ensemble approach is used to train the binary classifier using pre-processed recorded data packets to detect the anomaly and prevent the IoT networks from zero-day attacks. The positive class performance metrics of the model resulted in an accuracy of 98.27%, a precision of 96.40%, and a recall of 95.70%. The simulation results prove the effectiveness of the proposed model against cyber threats, thus making it suitable for critical applications for the IoT. |
| format |
article |
| author |
Parag Verma Ankur Dumka Rajesh Singh Alaknanda Ashok Anita Gehlot Praveen Kumar Malik Gurjot Singh Gaba Mustapha Hedabou |
| author_facet |
Parag Verma Ankur Dumka Rajesh Singh Alaknanda Ashok Anita Gehlot Praveen Kumar Malik Gurjot Singh Gaba Mustapha Hedabou |
| author_sort |
Parag Verma |
| title |
A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments |
| title_short |
A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments |
| title_full |
A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments |
| title_fullStr |
A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments |
| title_full_unstemmed |
A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments |
| title_sort |
novel intrusion detection approach using machine learning ensemble for iot environments |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/e69a2d2cf64141fb848fb57c6349d712 |
| work_keys_str_mv |
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