Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT)
The Internet of Things (IoT) has established itself as a multibillion-dollar business in recent years. Despite its obvious advantages, the widespread nature of IoT renders it insecure and a potential target for cyber-attacks. Furthermore, these devices broad connectivity and dynamic heterogeneous na...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9718242/ |
| _version_ | 1818335303694286848 |
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| author | Mohammed Saleh Ali Muthanna Reem Alkanhel Ammar Muthanna Ahsan Rafiq Wadhah Ahmed Muthanna Abdullah |
| author_facet | Mohammed Saleh Ali Muthanna Reem Alkanhel Ammar Muthanna Ahsan Rafiq Wadhah Ahmed Muthanna Abdullah |
| author_sort | Mohammed Saleh Ali Muthanna |
| collection | DOAJ |
| description | The Internet of Things (IoT) has established itself as a multibillion-dollar business in recent years. Despite its obvious advantages, the widespread nature of IoT renders it insecure and a potential target for cyber-attacks. Furthermore, these devices broad connectivity and dynamic heterogeneous nature can open up a new surface of attack for refined malware attacks. There is a critical need to protect the IoT environment from such attacks and malware. Therefore this research aims to propose an intelligent, SDN-enabled hybrid framework leveraging Cuda Long Short Term Memory Gated Recurrent Unit (cuLSTMGRU) for efficient threat detection in IoT environments. To properly assess the proposed system, a state-of-the-art IoT-based dataset and standard evaluation metrics were used. The proposed model achieved 99.23 % detection accuracy with a low false-positive rate. For further verification, we compare the proposed model results with two of our constructed models (i.e., cuBLSTM and cuGRUDNN) and current benchmark algorithms. The proposed model outclassed the other models regarding speed efficiency, detection accuracy, precision, and other standard evaluation metrics. Finally, the proposed work employed 10-fold cross-validation to ensure that the results were completely unbiased. |
| first_indexed | 2024-12-13T14:21:17Z |
| format | Article |
| id | doaj.art-7a8e793c774741d9864b201d6fc3ef75 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T14:21:17Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7a8e793c774741d9864b201d6fc3ef752022-12-21T23:42:06ZengIEEEIEEE Access2169-35362022-01-0110227562276810.1109/ACCESS.2022.31537169718242Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT)Mohammed Saleh Ali Muthanna0https://orcid.org/0000-0002-1165-7812Reem Alkanhel1https://orcid.org/0000-0001-6395-4723Ammar Muthanna2Ahsan Rafiq3Wadhah Ahmed Muthanna Abdullah4Institute of Computer Technologies and Information Security, Southern Federal University, Taganrog, RussiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), Moscow, RussiaCollege of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaDepartment of Mathematics & Mechanics, Saint Petersburg State University, Saint Petersburg, RussiaThe Internet of Things (IoT) has established itself as a multibillion-dollar business in recent years. Despite its obvious advantages, the widespread nature of IoT renders it insecure and a potential target for cyber-attacks. Furthermore, these devices broad connectivity and dynamic heterogeneous nature can open up a new surface of attack for refined malware attacks. There is a critical need to protect the IoT environment from such attacks and malware. Therefore this research aims to propose an intelligent, SDN-enabled hybrid framework leveraging Cuda Long Short Term Memory Gated Recurrent Unit (cuLSTMGRU) for efficient threat detection in IoT environments. To properly assess the proposed system, a state-of-the-art IoT-based dataset and standard evaluation metrics were used. The proposed model achieved 99.23 % detection accuracy with a low false-positive rate. For further verification, we compare the proposed model results with two of our constructed models (i.e., cuBLSTM and cuGRUDNN) and current benchmark algorithms. The proposed model outclassed the other models regarding speed efficiency, detection accuracy, precision, and other standard evaluation metrics. Finally, the proposed work employed 10-fold cross-validation to ensure that the results were completely unbiased.https://ieeexplore.ieee.org/document/9718242/Deep learningnetwork securityintrusion detectionsoftware-defined networkIoT |
| spellingShingle | Mohammed Saleh Ali Muthanna Reem Alkanhel Ammar Muthanna Ahsan Rafiq Wadhah Ahmed Muthanna Abdullah Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT) IEEE Access Deep learning network security intrusion detection software-defined network IoT |
| title | Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT) |
| title_full | Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT) |
| title_fullStr | Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT) |
| title_full_unstemmed | Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT) |
| title_short | Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT) |
| title_sort | towards sdn enabled intelligent intrusion detection system for internet of things iot |
| topic | Deep learning network security intrusion detection software-defined network IoT |
| url | https://ieeexplore.ieee.org/document/9718242/ |
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