Machine Learning Based Classifier for Service Function Chains

Authors

  • Habib Allah Khosravi Department of Computer Engineering, Faculty of Engineering, University of Qom, Iran
  • Yaghoub Farjami Department of Computer Engineering, Faculty of Engineering, University of Qom, Iran

DOI:

https://doi.org/10.31577/cai_2020_3_410

Keywords:

Service function chaining, classifier, machine learning, catboost

Abstract

Using service function chains, Internet Service Providers can customize the use of service functions that process the network flows belonging to their customers. Each network flow is injected into a service chain according to the flow features. Since most of the malicious applications try not to get the proper analysis by imitating some valid and famous applications, classification based on simple flow features may waste processing power by using inappropriate service chains for evasive flows. In this paper, we have explored an application-aware classification approach using machine learning methods. Using CatBoost as a machine learning method, a model is created and used for traffic classification. We have provided some statistical reports on how this approach is compared with simple flow feature-based approaches in malicious environments and how feature selection can impact classification correctness. Choosing the most suitable number of features at the right time can beat traditional approaches in classification quality and provide better results in the service function chaining environment.

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Published

2020-12-16

How to Cite

Khosravi, H. A., & Farjami, Y. (2020). Machine Learning Based Classifier for Service Function Chains. COMPUTING AND INFORMATICS, 39(3), 410–438. https://doi.org/10.31577/cai_2020_3_410