Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction
A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/20/7896 |
| _version_ | 1827648055513448448 |
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| author | Naveed Ahmed Asri bin Ngadi Johan Mohamad Sharif Saddam Hussain Mueen Uddin Muhammad Siraj Rathore Jawaid Iqbal Maha Abdelhaq Raed Alsaqour Syed Sajid Ullah Fatima Tul Zuhra |
| author_facet | Naveed Ahmed Asri bin Ngadi Johan Mohamad Sharif Saddam Hussain Mueen Uddin Muhammad Siraj Rathore Jawaid Iqbal Maha Abdelhaq Raed Alsaqour Syed Sajid Ullah Fatima Tul Zuhra |
| author_sort | Naveed Ahmed |
| collection | DOAJ |
| description | A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS. |
| first_indexed | 2024-03-09T19:31:02Z |
| format | Article |
| id | doaj.art-c3183e3a3b904bfbb02cda5ae6c1c4d5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:31:02Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c3183e3a3b904bfbb02cda5ae6c1c4d52023-11-24T02:28:05ZengMDPI AGSensors1424-82202022-10-012220789610.3390/s22207896Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research DirectionNaveed Ahmed0Asri bin Ngadi1Johan Mohamad Sharif2Saddam Hussain3Mueen Uddin4Muhammad Siraj Rathore5Jawaid Iqbal6Maha Abdelhaq7Raed Alsaqour8Syed Sajid Ullah9Fatima Tul Zuhra10School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaSchool of Digital Science, University Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, BruneiCollege of Computing and Information Technology, University of Doha For Science and Technology, Doha 24449, QatarDepartment of Computer Science, Capital University of Science and Technology, Islamabad 44000, PakistanDepartment of Computer Science, Capital University of Science and Technology, Islamabad 44000, PakistanDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Technology, College of Computing and Informatics, Saudi Electronic University, Riyadh 93499, Saudi ArabiaDepartment of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, NorwaySchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaA revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.https://www.mdpi.com/1424-8220/22/20/7896software defined networkintrusion detection systemsmachine learningdeep learningsecurity attacks |
| spellingShingle | Naveed Ahmed Asri bin Ngadi Johan Mohamad Sharif Saddam Hussain Mueen Uddin Muhammad Siraj Rathore Jawaid Iqbal Maha Abdelhaq Raed Alsaqour Syed Sajid Ullah Fatima Tul Zuhra Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction Sensors software defined network intrusion detection systems machine learning deep learning security attacks |
| title | Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction |
| title_full | Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction |
| title_fullStr | Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction |
| title_full_unstemmed | Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction |
| title_short | Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction |
| title_sort | network threat detection using machine deep learning in sdn based platforms a comprehensive analysis of state of the art solutions discussion challenges and future research direction |
| topic | software defined network intrusion detection systems machine learning deep learning security attacks |
| url | https://www.mdpi.com/1424-8220/22/20/7896 |
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