Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning
The orchestration of software-defined networks (SDN) and the internet of things (IoT) has revolutionized the computing fields. These include the broad spectrum of connectivity to sensors and electronic appliances beyond standard computing devices. However, these networks are still vulnerable to botn...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/24/9837 |
| _version_ | 1797455348981825536 |
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| author | Worku Gachena Negera Friedhelm Schwenker Taye Girma Debelee Henock Mulugeta Melaku Yehualashet Megeresa Ayano |
| author_facet | Worku Gachena Negera Friedhelm Schwenker Taye Girma Debelee Henock Mulugeta Melaku Yehualashet Megeresa Ayano |
| author_sort | Worku Gachena Negera |
| collection | DOAJ |
| description | The orchestration of software-defined networks (SDN) and the internet of things (IoT) has revolutionized the computing fields. These include the broad spectrum of connectivity to sensors and electronic appliances beyond standard computing devices. However, these networks are still vulnerable to botnet attacks such as distributed denial of service, network probing, backdoors, information stealing, and phishing attacks. These attacks can disrupt and sometimes cause irreversible damage to several sectors of the economy. As a result, several machine learning-based solutions have been proposed to improve the real-time detection of botnet attacks in SDN-enabled IoT networks. The aim of this review is to investigate research studies that applied machine learning techniques for deterring botnet attacks in SDN-enabled IoT networks. Initially the first major botnet attacks in SDN-IoT networks have been thoroughly discussed. Secondly a commonly used machine learning techniques for detecting and mitigating botnet attacks in SDN-IoT networks are discussed. Finally, the performance of these machine learning techniques in detecting and mitigating botnet attacks is presented in terms of commonly used machine learning models’ performance metrics. Both classical machine learning (ML) and deep learning (DL) techniques have comparable performance in botnet attack detection. However, the classical ML techniques require extensive feature engineering to achieve optimal features for efficient botnet attack detection. Besides, they fall short of detecting unforeseen botnet attacks. Furthermore, timely detection, real-time monitoring, and adaptability to new types of attacks are still challenging tasks in classical ML techniques. These are mainly because classical machine learning techniques use signatures of the already known malware both in training and after deployment. |
| first_indexed | 2024-03-09T15:53:06Z |
| format | Article |
| id | doaj.art-ff33152ff8cf4a1890ab90183799baf5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:53:06Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ff33152ff8cf4a1890ab90183799baf52023-11-24T17:56:07ZengMDPI AGSensors1424-82202022-12-012224983710.3390/s22249837Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine LearningWorku Gachena Negera0Friedhelm Schwenker1Taye Girma Debelee2Henock Mulugeta Melaku3Yehualashet Megeresa Ayano4Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa 445, EthiopiaInstitute of Neural Information, University of Ulm, 89069 Ulm, GermanyEthiopian Artificial Intelligence Institute, Addis Ababa 40782, EthiopiaAddis Ababa Institute of Technology, Addis Ababa University, Addis Ababa 445, EthiopiaEthiopian Artificial Intelligence Institute, Addis Ababa 40782, EthiopiaThe orchestration of software-defined networks (SDN) and the internet of things (IoT) has revolutionized the computing fields. These include the broad spectrum of connectivity to sensors and electronic appliances beyond standard computing devices. However, these networks are still vulnerable to botnet attacks such as distributed denial of service, network probing, backdoors, information stealing, and phishing attacks. These attacks can disrupt and sometimes cause irreversible damage to several sectors of the economy. As a result, several machine learning-based solutions have been proposed to improve the real-time detection of botnet attacks in SDN-enabled IoT networks. The aim of this review is to investigate research studies that applied machine learning techniques for deterring botnet attacks in SDN-enabled IoT networks. Initially the first major botnet attacks in SDN-IoT networks have been thoroughly discussed. Secondly a commonly used machine learning techniques for detecting and mitigating botnet attacks in SDN-IoT networks are discussed. Finally, the performance of these machine learning techniques in detecting and mitigating botnet attacks is presented in terms of commonly used machine learning models’ performance metrics. Both classical machine learning (ML) and deep learning (DL) techniques have comparable performance in botnet attack detection. However, the classical ML techniques require extensive feature engineering to achieve optimal features for efficient botnet attack detection. Besides, they fall short of detecting unforeseen botnet attacks. Furthermore, timely detection, real-time monitoring, and adaptability to new types of attacks are still challenging tasks in classical ML techniques. These are mainly because classical machine learning techniques use signatures of the already known malware both in training and after deployment.https://www.mdpi.com/1424-8220/22/24/9837botnetssoftware defined networksinternet of thingsmachine learning |
| spellingShingle | Worku Gachena Negera Friedhelm Schwenker Taye Girma Debelee Henock Mulugeta Melaku Yehualashet Megeresa Ayano Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning Sensors botnets software defined networks internet of things machine learning |
| title | Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning |
| title_full | Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning |
| title_fullStr | Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning |
| title_full_unstemmed | Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning |
| title_short | Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning |
| title_sort | review of botnet attack detection in sdn enabled iot using machine learning |
| topic | botnets software defined networks internet of things machine learning |
| url | https://www.mdpi.com/1424-8220/22/24/9837 |
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