Machine-Learning Techniques for Detecting Attacks in SDN
Open accessAbstract
With the advent of Software Defined Networks (SDNs), there has been a rapid advancement in the area of cloud computing. It is now scalable, cheaper, and easier to manage. However, SDNs are more prone to security vulner- abilities as compared to legacy systems. Therefore, machine- learning techniques are now deployed in the SDN infrastructure for the detection of malicious traffic. In this paper, we provide a systematic benchmarking analysis of the existing machine- learning techniques for the detection of malicious traffic in SDNs. We identify the limitations in these classical machine- learning based methods, and lay the foundation for a more robust framework. Our experiments are performed on a publicly available dataset of Intrusion Detection Systems (IDSs).
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Conventional approaches for preventing DoS attacks in SDNs are not helpful, as they largely depend upon the flow characteristics of the packets, allowing attackers to deceive the system with slight modifications to the traffic patterns.
“Conventional approaches for preventing DoS attacks in SDNs are not helpful, as they largely depend upon the flow characteristics of the packets, allowing the attackers to deceive the system with slight modifications to the traffic patterns (e.g., through changing packet headers to make it appear like legitimate traffic) [152].”
From Zero Trust Architecture (ZTA): A Comprehensive Survey (2022) · cites this paper as [152]
SDN platforms are more susceptible to threats because of the separation that exists between their control and data planes.
“As described in [152], SDN platforms are more susceptible to threats because of the separation that exists between their control and data planes.”
From Zero Trust Architecture (ZTA): A Comprehensive Survey (2022) · cites this paper as [152]
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Shankaraiah, S., & Shashank, S. A. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-6605-6_38
A Siamese-Based Approach for Network Intrusion Detection Systems in Software-Defined Networks
Nguyen, D. H., Tran, N. K., & Le‐Khac, N. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8296-7_14
Self-citationResearch on Network Abnormal Behavior Recognition Model Based on Parallel Decision Tree Algorithm
Hu, Z., Tan, Y. S., Cheng, A., & Zeng, G. (2023) · International Conference on Compute and Data Analysis (ICCDA) · doi:10.1145/3629264.3629272
Detection Method for Software Defined Network Intrusion
Shabu, S. L. J., K, N. G., Ram, N. K. S., Refonaa, J., & Grace, L. K. J. (2024) · International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · doi:10.1109/icicv62344.2024.00125
MLDAS : Machine Learning Dynamic Algorithm Selection for Software‐Defined Networking Security
Benlloch, P. M., Romero, Ó., Leon, A., & Lloret, J. (2026) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70637
Multi-Class Network Intrusion Detection Using Flow-Based Features: A Machine Learning Approach
Isyaku, B., Garba, S., Faruk, N., & Alhassan, I. M. (2025) · IEEE Connecting the Unconnected Europe, Middle East, and Africa Regional Summit (CTUS-EMEA) · doi:10.1109/ctus-emea68989.2025.11592252
- Authors
- Elsayed, M. S., Le-Khac, N.-A., Dev, S., & Jurcut, A. D.
- Affiliations
- University College Dublin (IE)
- Science Foundation Ireland (IE)
- Venue
- 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 277-281
- OpenAlex
- W2977374708
- Semantic Scholar
- e7427e26341e63df81929cdaf3ff8deef11bc6bd
- Cited by
- 72
- References
- 30
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 13 · 2023: 14 · 2022: 14 · 2021: 16 · 2020: 7 · 2019: 1
- FWCI
- 8.22
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Conference on Computer Science and Network Technology (ICCSNT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- software-defined networking, intrusion detection, malicious traffic detection, network security, machine learning benchmarking, intrusion detection systems, network attack detection
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