A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

Article


Latif, Z., Umer, Q., Lee, C., Sharif, K., Li, F. and Biswas, S. 2022. A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks. Sensors. 22 (21), p. Art. 8434. https://doi.org/10.3390/s22218434
AuthorsLatif, Z., Umer, Q., Lee, C., Sharif, K., Li, F. and Biswas, S.
Abstract

Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic. Due to this, the controller may install the flow rules against the anomalies, reducing the overall network performance. These anomalies may indicate threats to the network and decrease its performance and security. Machine learning (ML) approaches can identify such traffic flow patterns and predict the systems’ impending threats. We propose an ML-based service to predict traffic anomalies for software-defined networks in this work. We first create a large dataset for network traffic by modeling a programmable data center with a signature-based intrusion-detection system. The feature vectors are pre-processed and are constructed against each flow request by the forwarding element. Then, we input the feature vector of each request to a machine learning classifier for training to predict anomalies. Finally, we use the holdout cross-validation technique to evaluate the proposed approach. The evaluation results specify that the proposed approach is highly accurate. In contrast to baseline approaches (random prediction and zero rule), the performance improvement of the proposed approach in average accuracy, precision, recall, and f-measure is (54.14%, 65.30%, 81.63%, and 73.70%) and (4.61%, 11.13%, 9.45%, and 10.29%), respectively.

Keywordssoftware-defined networking (SDN); anomaly prediction; OpenFlow; Machine Learning
JournalSensors
Journal citation22 (21), p. Art. 8434
ISSN1424-8220
Year2022
PublisherMDPI
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.3390/s22218434
Web address (URL)https://www.mdpi.com/1424-8220/22/21/8434
Publication dates
Online02 Nov 2022
Publication process dates
Accepted29 Oct 2022
Deposited22 Jun 2023
FunderNational Research Foundation of Korea
Copyright holder© 2022, The Author(s)
Permalink -

https://repository.uel.ac.uk/item/8w350

Download files


Publisher's version
sensors-22-08434-with-cover.pdf
License: CC BY 4.0
File access level: Anyone

  • 37
    total views
  • 21
    total downloads
  • 2
    views this month
  • 1
    downloads this month

Export as

Related outputs

Interoperability Benefits and Challenges in Smart City Services: Blockchain as a Solution
Biswas, S., Yao, Z., Yan, L., Alqhatani, A., Bairagi, A. K., Asiri, F. and Masud, M. 2023. Interoperability Benefits and Challenges in Smart City Services: Blockchain as a Solution. Electronics. 12 (4), p. Art. 1036. https://doi.org/https://doi.org/10.3390/electronics12041036
Impact learning: A learning method from feature’s impact and competition
Prottasha, N. J., Murad, S. A., Muzahid, A. J. M., Rana, M., Kowsher, M., Adhikary, A., Biswas, S. and Bairagi, A. K. 2023. Impact learning: A learning method from feature’s impact and competition. Journal of Computational Science. 69 (Art. 102011). https://doi.org/10.1016/j.jocs.2023.102011
Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis
Alghamdi, A., Zhu, J., Yin, G., Shorfuzzaman, M., Alsufyani, N., Alyami, S. and Biswas, S. 2022. Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis. Sensors. 22 (18), p. 6786. https://doi.org/10.3390/s22186786