Version 1
: Received: 18 December 2023 / Approved: 19 December 2023 / Online: 19 December 2023 (11:18:08 CET)
How to cite:
Janabi, A.H.K.; Kanakis, T.; Johnson, M. A Survey of Intrusion Detection Systems based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges. Preprints2023, 2023121449. https://doi.org/10.20944/preprints202312.1449.v1
Janabi, A.H.K.; Kanakis, T.; Johnson, M. A Survey of Intrusion Detection Systems based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges. Preprints 2023, 2023121449. https://doi.org/10.20944/preprints202312.1449.v1
Janabi, A.H.K.; Kanakis, T.; Johnson, M. A Survey of Intrusion Detection Systems based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges. Preprints2023, 2023121449. https://doi.org/10.20944/preprints202312.1449.v1
APA Style
Janabi, A.H.K., Kanakis, T., & Johnson, M. (2023). A Survey of Intrusion Detection Systems based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges. Preprints. https://doi.org/10.20944/preprints202312.1449.v1
Chicago/Turabian Style
Janabi, A.H.K., Triantafyllos Kanakis and Mark Johnson. 2023 "A Survey of Intrusion Detection Systems based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges" Preprints. https://doi.org/10.20944/preprints202312.1449.v1
Abstract
: Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance, it brings both opportunities and challenges. One of the major challenges is the need to secure networks and sensitive data from various malicious activities. Traditional networks have evolved to include Software Defined Network (SDN), which offers a more flexible and programmable framework. Researchers should focus on detecting attacks in SDN because SDN networks are becoming more popular and attractive targets for clients due to their programmability and dynamic nature. The centralised controller, known as the backbone of an SDN, becomes a single point of failure and a potential target for attackers if not properly secured. Researchers need to emphasise the detection of attacks in SDN in order to mitigate these risks. By understanding the potential vulnerabilities and attack methods specific to SDN, researchers can develop effective detection approaches and propose countermeasures. This methodology helps protect the network from potential threats and minimises the impact of successful attacks. Therefore, this survey paper provides a review of Intrusion Detection Systems (IDSs) in Software-Defined Networks (SDN) to provide a thorough understanding of SDN security issues.
Keywords
Software-Defined Network (SDN), Intrusion Detection System (IDS), Machine Learning (ML), Deep Learning (DL), non-Deep Learning, OpenFlow, Control Plane, Data Plane, attack, security, and challenges.
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
