A novel anomaly detection model for the industrial Internet of Things using machine learning techniques
In recent decades, the pervasive integration of the Internet of Things (IoT) technologies has revolutionized various sectors, including industry 4.0, telecommunications, cloud computing, and healthcare systems. Industry 4.0 applications, characterized by real-time data exchange, increased reliance o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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National Aerospace University «Kharkiv Aviation Institute»
2024-02-01
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| Series: | Радіоелектронні і комп'ютерні системи |
| Subjects: | |
| Online Access: | http://nti.khai.edu/ojs/index.php/reks/article/view/2282 |
| _version_ | 1797220394451599360 |
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| author | Lahcen Idouglid Said Tkatek Khalid Elfayq Azidine Guezzaz |
| author_facet | Lahcen Idouglid Said Tkatek Khalid Elfayq Azidine Guezzaz |
| author_sort | Lahcen Idouglid |
| collection | DOAJ |
| description | In recent decades, the pervasive integration of the Internet of Things (IoT) technologies has revolutionized various sectors, including industry 4.0, telecommunications, cloud computing, and healthcare systems. Industry 4.0 applications, characterized by real-time data exchange, increased reliance on automation, and limited computational resources at the edge, have reshaped global business dynamics, aiming to innovate business models through enhanced automation technologies. However, ensuring security in these environments remains a critical challenge, with real-time data streams introducing vulnerabilities to zero-day attacks and limited resources at the edge demanding efficient intrusion detection solutions. This study addresses this pressing need by proposing a novel intrusion detection model (IDS) specifically designed for Industry 4.0 environments. The proposed IDS leverages a Random Forest classifier with Principal Component Analysis (PCA) for feature selection. This approach addresses the challenges of real-time data processing and resource limitations while offering high accuracy. Based on the Bot-IoT dataset, the model achieves a competitive accuracy of 98.9% and a detection rate of 97.8%, outperforming conventional methods. This study demonstrates the effectiveness of the proposed IDS for securing Industry 4.0 ecosystems, offering valuable contributions to the field of cybersecurity. |
| first_indexed | 2024-04-24T12:48:51Z |
| format | Article |
| id | doaj.art-4aee5718d63d47d3a47717592a50d2ad |
| institution | Directory Open Access Journal |
| issn | 1814-4225 2663-2012 |
| language | English |
| last_indexed | 2024-04-24T12:48:51Z |
| publishDate | 2024-02-01 |
| publisher | National Aerospace University «Kharkiv Aviation Institute» |
| record_format | Article |
| series | Радіоелектронні і комп'ютерні системи |
| spelling | doaj.art-4aee5718d63d47d3a47717592a50d2ad2024-04-06T08:29:50ZengNational Aerospace University «Kharkiv Aviation Institute»Радіоелектронні і комп'ютерні системи1814-42252663-20122024-02-012024114315110.32620/reks.2024.1.122108A novel anomaly detection model for the industrial Internet of Things using machine learning techniquesLahcen Idouglid0Said Tkatek1Khalid Elfayq2Azidine Guezzaz3Ibn Tofail University KenitraIbn Tofail University KenitraIbn Tofail University KenitraCadi Ayyad University, EssaouiraIn recent decades, the pervasive integration of the Internet of Things (IoT) technologies has revolutionized various sectors, including industry 4.0, telecommunications, cloud computing, and healthcare systems. Industry 4.0 applications, characterized by real-time data exchange, increased reliance on automation, and limited computational resources at the edge, have reshaped global business dynamics, aiming to innovate business models through enhanced automation technologies. However, ensuring security in these environments remains a critical challenge, with real-time data streams introducing vulnerabilities to zero-day attacks and limited resources at the edge demanding efficient intrusion detection solutions. This study addresses this pressing need by proposing a novel intrusion detection model (IDS) specifically designed for Industry 4.0 environments. The proposed IDS leverages a Random Forest classifier with Principal Component Analysis (PCA) for feature selection. This approach addresses the challenges of real-time data processing and resource limitations while offering high accuracy. Based on the Bot-IoT dataset, the model achieves a competitive accuracy of 98.9% and a detection rate of 97.8%, outperforming conventional methods. This study demonstrates the effectiveness of the proposed IDS for securing Industry 4.0 ecosystems, offering valuable contributions to the field of cybersecurity.http://nti.khai.edu/ojs/index.php/reks/article/view/2282industry 4.0 securityiiotiotanomaly detectionfeature selectionrandom forest |
| spellingShingle | Lahcen Idouglid Said Tkatek Khalid Elfayq Azidine Guezzaz A novel anomaly detection model for the industrial Internet of Things using machine learning techniques Радіоелектронні і комп'ютерні системи industry 4.0 security iiot iot anomaly detection feature selection random forest |
| title | A novel anomaly detection model for the industrial Internet of Things using machine learning techniques |
| title_full | A novel anomaly detection model for the industrial Internet of Things using machine learning techniques |
| title_fullStr | A novel anomaly detection model for the industrial Internet of Things using machine learning techniques |
| title_full_unstemmed | A novel anomaly detection model for the industrial Internet of Things using machine learning techniques |
| title_short | A novel anomaly detection model for the industrial Internet of Things using machine learning techniques |
| title_sort | novel anomaly detection model for the industrial internet of things using machine learning techniques |
| topic | industry 4.0 security iiot iot anomaly detection feature selection random forest |
| url | http://nti.khai.edu/ojs/index.php/reks/article/view/2282 |
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