Machine Learning for Management in Software-defined Networks: A Systematic Literature Review

다국어 초록 (Multilingual Abstract)

Software-Defined Networking (SDN) has emerged as a new paradigm for managing data networks, and Machine Learning (ML) techniques have become relevant in the scientific community to solve management problems. Research on using these two variables has increased in recent years. Therefore, a systematic literature review based on Kitchenham’s guidelines and PRISMA guidelines is necessary. The review included publications from between 2016 and 2021.
The study recorded 21,743 primary articles, and after applying rigorous exclusion and quality criteria, 81 articles were obtained. The results show the most productive authors, such as Julong, as well as the relationships between the most productive authors and the keywords “SDN” and “Machine Learning,” which are the most used among researchers.

참고문헌 (Reference)

1 S. Khamaiseh, "vSwitchGuard:Defending OpenFlow Switches Against Saturation Attacks" 851-860, 2020

2 C. B. Zerbini, "Wavelet against random forest for anomaly mitigation in software-defined networking" 80 : 138-153, 2019

3 R. Swami, "Voting-based intrusion detection framework for securing software-defined networks" 32 (32): e5927-, 2020

4 K. Rusek, "Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN" Association for Computing Machinery 140-151, 2019

5 J. Pei, "Two-Phase Virtual Network Function Selection and Chaining Algorithm Based on Deep Learning in SDN/NFV-Enabled Networks" 38 (38): 1102-1117, 2020

6 F. Benayas, "Towards an autonomic Bayesian fault diagnosis service for SDN environments based on a big data infrastructure" 7-13, 2018

7 E. Unal, "Towards Prediction of Security Attacks on Software Defined Networks: A Big Data Analytic Approach" 4582-4588, 2018

8 M. Z. F. Audah, "Towards Efficient and Scalable Machine Learning-Based QoS Traffic Classification in Software-Defined Network" 217-229, 2019

9 M. U. Öney, "The Use of Artificial Neural Networks in Network Intrusion Detection: A Systematic Review" 2019

10 P. Sun, "TIDE : Time-relevant deep reinforcement learning for routing optimization" 99 : 401-409, 2019

11 J. N. Witanto, "Software-Defined Networking Application with Deep Deterministic Policy Gradient" 176-179, 2019

12 P. Sun, "SmartFCT: Improving power-efficiency for data center networks with deep reinforcement learning" 179 : 107255-, 2020

13 P. Sun, "ScaleDRL : A Scalable Deep Reinforcement Learning Approach for Traffic Engineering in SDN with Pinning Control" 190 : 107891-, 2021

14 C. -C. Liu, "SVM-based Classification Mechanism and Its Application in SDN Networks" 45-49, 2018

15 T. -Y. Mu, "SDN Flow Entry Management Using Reinforcement Learning" Association for Computing Machinery 13 (13): 11-, 2018

16 K. Rusek, "RouteNet : Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN" 38 (38): 2260-2270, 2020

17 P. Sun, "RNN Deep Reinforcement Learning for Routing Optimization" 285-289, 2018

18 M. Ibrar, "PrePass-Flow: A Machine Learning based technique to minimize ACL policy violation due to links failure in hybrid SDN" 184 : 107706-, 2021

19 T. Chin, "Phishlimiter : A Phishing Detection and Mitigation Approach Using Software-Defined Networking" 6 : 42516-42531, 2018

20 Z. Ma, "Parallel Architectures, Algorithms and Programming" 368-379, 2020

21 T. Bakhshi, "OpenFlow-enabled user traffic profiling in campus software defined networks" 1-8, 2016

22 S. S. Volkov, "Network attacks classification using Long Short-term memory based neural networks in Software-Defined Networks" 178 : 394-403, 2020

23 R. Durner, "Network Function Offloading Through Classification of Elephant Flows" 17 (17): 807-820, 2020

24 A. T. Kyaw, "Machine-Learning Based DDOS Attack Classifier in Software Defined Network" 431-434, 2020

25 A. Abubakar, "Machine learning based intrusion detection system for software defined networks" 138-143, 2017

26 S. Gangadhar, "Machine learning aided traffic tolerance to improve resilience for software defined networks" 1-7, 2017

27 G. Cusack, "Machine Learning-Based Detection of Ransomware Using SDN" Association for Computing Machinery 1-6, 2018

28 J. Liu, "Machine Learning in Software Defined Network" 1114-1120, 2019

29 A. B. Nassif, "Machine Learning for Cloud Security : A Systematic Review" 9 : 20717-20735, 2021

30 A. B. Nassif, "Machine Learning for Anomaly Detection : A Systematic Review" 9 : 78658-78700, 2021

31 A. Ben Letaifa, "ML based QoE enhancement in SDN context: Video streaming case" 103-108, 2017

32 M. Aibin, "LSTM for Cloud Data Centers Resource Allocation in Software-Defined Optical Networks" 0162-0167, 2020

33 F. Alhaidari, "Intelligent Software-Defined Network for Cognitive Routing Optimization using Deep Extreme Learning Machine Approach" 67 (67): 1269-1285, 2021

34 Q. Zhang, "Intelligent Content-Aware Traffic Engineering for SDN : An AI-Driven Approach" 34 (34): 186-193, 2020

35 E. F. Castillo, "IPro: An approach for intelligent SDN monitoring" 170 : 107108-, 2020

36 S. Garg, "Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN : A Social Multimedia Perspective" 21 (21): 566-578, 2019

37 J. Malik, "Hybrid Deep Learning : An Efficient Reconnaissance and Surveillance Detection Mechanism in SDN" 8 : 134695-134706, 2020

38 G. Kaur, "Hybrid Approach for detecting DDOS Attacks in Software Defined Networks" 1-6, 2019

39 B. Kitchenham, "Guidelines for performing Systematic Literature Reviews in Software Engineering" Keele University 2007

40 N. Satheesh, "Flow-based anomaly intrusion detection using machine learning model with software defined networking for OpenFlow network" 79 : 103285-, 2020

41 T. Truong-Huu, "Fast and Adaptive Failure Recovery using Machine Learning in Software Defined Networks" 1-6, 2019

42 N. Bouacida, "Failure mitigation in software defined networking employing load type prediction" 1-7, 2017

43 H. An, "Dynamically Split the Traffic in Software Defined Network Based on Deep Reinforcement Learning" 806-811, 2020

44 Y. Park, "Distributed Security Network Functions against Botnet Attacks in Software-defined Networks" 1-7, 2018

45 A. Volkov, "Distributed Computer and Communication Networks" 27-40, 2019

46 S. Khamaiseh, "Detecting Saturation Attacks in SDN via Machine Learning" 1-8, 2019

47 A. Lazaris, "DeepFlow: a deep learning framework for software-defined measurement" 43-48, 2017

48 C. Zhang, "Deep learning-based network application classification for SDN: Deep learning-based network application classification for SDN" 29 (29): e3302-, 2018

49 E. H. Bouzidi, "Deep Reinforcement Learning Application for Network Latency Management in Software Defined Networks" 1-6, 2019

50 T. -H. Lee, "Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks" 1-6, 2020

51 Y. Qin, "Deep Learning Based Anomaly Detection Scheme in Software-Defined Networking" 1-4, 2019

52 M. M. Raikar, "Data Traffic Classification in Software Defined Networks(SDN)using supervised-learning" 171 : 2750-2759, 2020

53 C. Yu, "DROM : Optimizing the Routing in Software-Defined Networks With Deep Reinforcement Learning" 6 : 64533-64539, 2018

54 W. Liu, "DRL-R:Deep reinforcement learning approach for intelligent routing in software-defined data-center networks" 177 : 102865-, 2021

55 O. Rahman, "DDoS Attacks Detection and Mitigation in SDN Using Machine Learning" 184-189, 2019

56 R. ur Rasool, "CyberPulse++: A machine learning-based security framework for detecting link flooding attacks in software defined networks" 2021

57 W. -X. Liu, "Content Popularity Prediction and Caching for ICN : A Deep Learning Approach With SDN" 6 : 5075-5089, 2018

58 I. Bolodurina, "Comprehensive approach for optimization traffic routing and using network resources in a virtual data center" 136 : 62-71, 2018

59 J. Zhang, "CFR-RL : Traffic Engineering With Reinforcement Learning in SDN" 38 (38): 2249-2259, 2020

60 Y. Liu, "Blockchain and Machine Learning for Communications and Networking Systems" 22 (22): 1392-1431, 2020

61 H. A. Alamri, "Bandwidth Control Mechanism and Extreme Gradient Boosting Algorithm for Protecting Software-Defined Networks Against DDoS Attacks" 8 : 194269-194288, 2020

62 L.-H. Chang, "Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks" 5 (5): 4-, 2020

63 A. M. El-Shamy, "Anomaly Detection and Bottleneck Identification of The Distributed Application in Cloud Data Center using Software-Defined Networking" 2021

64 M. Latah, "An efficient flow-based multi-level hybrid intrusion detection system for software-defined networks" 3 (3): 261-271, 2020

65 P. Hadem, "An SDN-based Intrusion Detection System using SVM with Selective Logging for IP Traceback" 191 : 108015-, 2021

66 A. Lazaris, "An LSTM Framework for Software-Defined Measurement" 18 (18): 855-869, 2021

67 A. Prakash, "An Intelligent Software defined Network Controller for preventing Distributed Denial of Service Attack" 585-589, 2018

68 K. Lou, "An Elephant Flow Detection Method Based on Machine Learning" 11910 : 212-220, 2019

69 A. Rego, "Adapting reinforcement learning for multimedia transmission on SDN" 30 (30): 2019

70 TextBlob, "API Reference - TextBlob 0.16.0documentation"

71 A. Canovas, "A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN" 150 : 102498-, 2020

72 E. R. S., "A performance analysis of Software Defined Network based prevention on phishing attack in cyberspace using a deep machine learning with CANTINA approach(DMLCA)" 153 : 375-381, 2020

73 M. Latah, "A novel intelligent approach for detecting DoS flooding attacks in software-defined networks" 4 (4): 11-, 2018

74 M. K. Prasath, "A meta-heuristic Bayesian network classification for intrusion detection" 29 (29): e2047-, 2019

75 S. Petrangeli, "A machine learning-based framework for preventing video freezes in HTTP adaptive streaming" 94 : 78-92, 2017

76 T. T. Huong, "A global multipath load-balanced routing algorithm based on Reinforcement Learning in SDN" 1336-1341, 2019

77 Amarudin, "A Systematic Literature Review of Intrusion Detection System for Network Security: Research Trends, Datasets and Methods" 2020

78 J. Xie, "A Survey of Machine Learning Techniques Applied to Software Defined Networking(SDN) : Research Issues and Challenges" 21 (21): 393-430, 2019

79 A. M. Pradana, "A Simulation Of Load Balancing In Software Defined Network(Sdn)Based On Artificial Neural Networks Method" 15 (15): 748-758, 2020

80 W. Sun, "A QoS-guaranteed intelligent routing mechanism in software-defined networks" 185 : 107709-, 2021

81 S. S. Mohammed, "A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network" 1-8, 2018

82 S. Kumar, "A Machine Learning Approach for Traffic Flow Provisioning in Software Defined Networks" 602-607, 2020

83 K. S. Sahoo, "A Machine Learning Approach for Predicting DDoS Traffic in Software Defined Networks" 199-203, 2018

84 T. Abhiroop, "A Machine Learning Approach for Detecting DoS Attacks in SDN Switches" 1-6, 2018

85 A. M. R. Ruelas, "A Load Balancing Method based on Artificial Neural Networks for Knowledge-defined Data Center Networking" 106-109, 2018

86 M. V. O. Assis, "A GRU deep learning system against attacks in software defined networks" 177 : 102942-, 2021

87 P. Wang, "A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs" 760-765, 2016

88 J. A. Perez-Diaz, "A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning" 8 : 155859-155872, 2020

89 A. Alshamrani, "A Defense System for Defeating DDoS Attacks in SDN based Networks" 83-92, 2017

90 N. N. Tuan, "A DDoS attack mitigation scheme in ISP networks using machine learning based on SDN" 9 (9): 19-, 2020