Open Access

ARTICLE

Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

by Qinyue Wu, Hui Xu^*, Mengran Liu

School of Computer Science, Hubei University of Technology, Wuhan, 430068, China

* Corresponding Author: Hui Xu. Email:

(This article belongs to the Special Issue: Multimedia Encryption and Information Security)

Computers, Materials & Continua 2024, 78(3), 4091-4107. https://doi.org/10.32604/cmc.2024.048461

Received 08 December 2023; Accepted 25 January 2024; Issue published 26 March 2024

Abstract

Network traffic identification is critical for maintaining network security and further meeting various demands of network applications. However, network traffic data typically possesses high dimensionality and complexity, leading to practical problems in traffic identification data analytics. Since the original Dung Beetle Optimizer (DBO) algorithm, Grey Wolf Optimization (GWO) algorithm, Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution, an Improved Dung Beetle Optimizer (IDBO) algorithm is proposed for network traffic identification. Firstly, the Sobol sequence is utilized to initialize the dung beetle population, laying the foundation for finding the global optimal solution. Next, an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas, escaping from the local optimal solution, and converging more effectively towards a global optimal solution. Finally, an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence. With the improvements above, the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection, as so to find the optimal subset for K-Nearest Neighbor (KNN) classification. The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO, GWO, WOA, and PSO algorithms. The experimental results show that, compared with other algorithms, the accuracy and recall are improved by 1.53% and 0.88% in binary classification, and the Distributed Denial of Service (DDoS) class identification is the most effective in multi-classification, with an improvement of 5.80% and 0.33% for accuracy and recall, respectively. Therefore, the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.

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Keywords

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