A NEW APPROACH TO INTERNET TRAFFIC CLASSIFICATION: ARTIFICIAL BEE CLONING ALGORITHM - ONLINE SEQUENTIAL ANALYTICAL MACHINE-WAVELET (OSELM- WAVELET)

Abstract

This article discusses an important problem in network management and security—accurate and efficient classification of internet traffic. The need for real-time processing, as well as the complexity and everchanging nature of the network traffic, calls for both high accuracy and computational efficiency. To meet these demands, this study proposes a new approach that incorporates the Online Sequential Extreme Learning Machine (OSELM) and Wavelet Transform, which has been optimized by the Artificial Bee Cloning Algorithm. The proposed OSELM-Wavelet method uses Daubechies 4 wavelet transform to capture relevant frequency-domain features and, at the same time, preserve raw time-domain signals. This method improves the feature set by capturing time and frequency domain features simultaneously, boosting the input data by 800 features. Classification is carried out via the OSELM framework which supports efficient online training and inference and is thus suitable for real-time applications. The Artificial Bee Clony Algorithm is used to optimize the OSELM model parameters and improve the classification accuracy. This algorithm is inspired by the intelligent foraging behavior of bees and can effectively explore and exploit the parameter space for optimal solutions. The experiments bee algorithm-optimized OSELM-Wavelet conducted for internet traffic classification tasks demonstrated high accuracy and robustness.It outperforms conventional statistical and machine learning methods, particularly in situations demanding rapid adaptation and online learning. To summarize, the article combines OSELM-Wavelet with Artificial Bee Cloning Algorithm for Internet traffic classification, presenting a new solution. It aids in precisely and swiftly classifying traffic with minimal computation, thereby improving network security and management.