Efficient Intrusion Detection System for SDN Orchestrated Internet of Things
Internet of Things (IoT) can simply be defined as an extension of the current Internet system. It extends the human to human interconnection and intercommunication scenario of the Internet by including things, to bring anytime, anywhere, and anything communication. A discipline in networking evolvin...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2021/5593214 |
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| author | Esubalew M. Zeleke Henock M. Melaku Fikreselam G. Mengistu |
| author_facet | Esubalew M. Zeleke Henock M. Melaku Fikreselam G. Mengistu |
| author_sort | Esubalew M. Zeleke |
| collection | DOAJ |
| description | Internet of Things (IoT) can simply be defined as an extension of the current Internet system. It extends the human to human interconnection and intercommunication scenario of the Internet by including things, to bring anytime, anywhere, and anything communication. A discipline in networking evolving in parallel with IoT is Software Defined Networking (SDN). It is an important technology that is aimed to solve the different problems existing in the traditional network systems. It provides a new convenient home to address the different challenges existing in different network-based systems including IoT. One important security challenge prevailing in such SDN-based IoT (SDIoT) systems is guarantying service availability. The ever-increasing denial of service (DoS) attacks are responsible for such service denials. A centralized signature-based intrusion detection system (IDS) is proposed and developed in this work. Random Forest (RF) classifier is used for training the model. A very popular and recent benchmark dataset, CICIDS2017, has been used for training and validating the machine learning (ML) models. An accuracy result of 99.968% has been achieved by using only 12 features on Wednesday’s release of the dataset. This result is higher than the achieved accuracy results of related works considering the original CICIDS2017 dataset. A maximum cross-validated accuracy result of 99.713% has been achieved on the same release of the dataset. These developed models meet the basic requirement of a supervised IDS system developed for smart environments and can effectively be used in different IoT service scenarios. |
| format | Article |
| id | doaj-art-cd6a1484c7ba48d08307d4d3cef24301 |
| institution | OA Journals |
| issn | 2090-715X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-cd6a1484c7ba48d08307d4d3cef243012025-08-20T02:19:51ZengWileyJournal of Computer Networks and Communications2090-715X2021-01-01202110.1155/2021/5593214Efficient Intrusion Detection System for SDN Orchestrated Internet of ThingsEsubalew M. Zeleke0Henock M. Melaku1Fikreselam G. Mengistu2Department of Electrical and Computer EngineeringSchool of Electrical and Computer EngineeringFaculty of Electrical and Computer EngineeringInternet of Things (IoT) can simply be defined as an extension of the current Internet system. It extends the human to human interconnection and intercommunication scenario of the Internet by including things, to bring anytime, anywhere, and anything communication. A discipline in networking evolving in parallel with IoT is Software Defined Networking (SDN). It is an important technology that is aimed to solve the different problems existing in the traditional network systems. It provides a new convenient home to address the different challenges existing in different network-based systems including IoT. One important security challenge prevailing in such SDN-based IoT (SDIoT) systems is guarantying service availability. The ever-increasing denial of service (DoS) attacks are responsible for such service denials. A centralized signature-based intrusion detection system (IDS) is proposed and developed in this work. Random Forest (RF) classifier is used for training the model. A very popular and recent benchmark dataset, CICIDS2017, has been used for training and validating the machine learning (ML) models. An accuracy result of 99.968% has been achieved by using only 12 features on Wednesday’s release of the dataset. This result is higher than the achieved accuracy results of related works considering the original CICIDS2017 dataset. A maximum cross-validated accuracy result of 99.713% has been achieved on the same release of the dataset. These developed models meet the basic requirement of a supervised IDS system developed for smart environments and can effectively be used in different IoT service scenarios.http://dx.doi.org/10.1155/2021/5593214 |
| spellingShingle | Esubalew M. Zeleke Henock M. Melaku Fikreselam G. Mengistu Efficient Intrusion Detection System for SDN Orchestrated Internet of Things Journal of Computer Networks and Communications |
| title | Efficient Intrusion Detection System for SDN Orchestrated Internet of Things |
| title_full | Efficient Intrusion Detection System for SDN Orchestrated Internet of Things |
| title_fullStr | Efficient Intrusion Detection System for SDN Orchestrated Internet of Things |
| title_full_unstemmed | Efficient Intrusion Detection System for SDN Orchestrated Internet of Things |
| title_short | Efficient Intrusion Detection System for SDN Orchestrated Internet of Things |
| title_sort | efficient intrusion detection system for sdn orchestrated internet of things |
| url | http://dx.doi.org/10.1155/2021/5593214 |
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