A New Hybrid Approach for Traffic Identification and Classification in Wireless Networks

Document Type : Original Article

Authors

1 Master's degree, Electrical and Computer Faculty, Malek Ashtar University of Technology, Tehran, Iran

2 Assistant Professor, Electrical and Computer Faculty, Malik Ashtar University of Technology, Tehran, Iran

Abstract

Using the ad hoc approach is one of the desirable options for configuration of wireless networks because of the features such as distributed management between nodes, facilitating their entry and exit into the network and the possibility of better mobility. This scheme leads to the dynamic behavior of the traffic generated by applications in such networks, which affects the issue of network management and traffic control between nodes. Identifying and classifying the network traffic can help to deal with these challenges in wireless networks. Because conventional traffic detection and classification methods are not able to provide proper performance with such traffic, applied machine-learning-based methods can improve the detection and classification performance. As the precision required to find a specific network traffic implies a high probability of detection, and the elimination of wrong decisions needs the false alarm rate reduction, in this paper a new hybrid method, based on the combination of machine learning methods is introduced to increase the accuracy and efficiency of identifying and classifying traffic in ad hoc wireless networks based on purposeful combination of various machine learning methods. The results show that in addition to improving the evaluation criteria of traffic classification, the proposed method increases the detection probability and reduces the false alarm rate, in comparison to the cases where only a single machine learning method is used.

Keywords

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References

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History

  • Receive Date: 19 April 2021
  • Revise Date: 11 September 2021
  • Accept Date: 29 September 2021
  • Publish Date: 23 September 2022

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