An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security
Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations. However, IIoT involves some security vulnerabilities that are more damaging than...
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| Format: | Article |
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Tsinghua University Press
2023-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020032 |
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| author | Mouaad Mohy-Eddine Azidine Guezzaz Said Benkirane Mourade Azrour Yousef Farhaoui |
| author_facet | Mouaad Mohy-Eddine Azidine Guezzaz Said Benkirane Mourade Azrour Yousef Farhaoui |
| author_sort | Mouaad Mohy-Eddine |
| collection | DOAJ |
| description | Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations. However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson’s Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18 s and 6.25 s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models. |
| format | Article |
| id | doaj-art-0961fc841b594dfdbebeac0c2aeb129b |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-0961fc841b594dfdbebeac0c2aeb129b2025-02-03T08:11:49ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-09-016327328710.26599/BDMA.2022.9020032An Ensemble Learning Based Intrusion Detection Model for Industrial IoT SecurityMouaad Mohy-Eddine0Azidine Guezzaz1Said Benkirane2Mourade Azrour3Yousef Farhaoui4Technology Higher School, Cadi Ayyad University, Essaouira 44000, Morocco.Technology Higher School, Cadi Ayyad University, Essaouira 44000, Morocco.Technology Higher School, Cadi Ayyad University, Essaouira 44000, Morocco.IDMS Team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 52000, Morocco.IDMS Team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 52000, Morocco.Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations. However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson’s Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18 s and 6.25 s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models.https://www.sciopen.com/article/10.26599/BDMA.2022.9020032industrial internet of things (iiot)isolation forestintrusion detection dystem (ids)intrusionpearson’s correlation coefficient (pcc)random forest |
| spellingShingle | Mouaad Mohy-Eddine Azidine Guezzaz Said Benkirane Mourade Azrour Yousef Farhaoui An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security Big Data Mining and Analytics industrial internet of things (iiot) isolation forest intrusion detection dystem (ids) intrusion pearson’s correlation coefficient (pcc) random forest |
| title | An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security |
| title_full | An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security |
| title_fullStr | An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security |
| title_full_unstemmed | An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security |
| title_short | An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security |
| title_sort | ensemble learning based intrusion detection model for industrial iot security |
| topic | industrial internet of things (iiot) isolation forest intrusion detection dystem (ids) intrusion pearson’s correlation coefficient (pcc) random forest |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020032 |
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