Internet Traffic Classification Model Based on A-DBSCAN Algorithm.

Network traffic classification has become more important with the rapid growth of the Internet and online applications. The rapid development of the Internet has enabled explosive growth of various network traffic. The challenge lies in how to classify and identify different categories of network traffic among these huge network traffic. The classification with the massive data network traffic suffers from noise and imbalanced data. Traditional classification algorithms are becoming less effective in handling these issues of the large number of traffic generated by these technologies. This paper proposes an advanced clustering model to enhance network traffic classification and improve the quality of services based on Advanced Density-Based Spatial Clustering of Applications with Noise (A-DBSCAN) with similarity and probability distance. A-DBSCAN with adaptive parameters are applied to identify clusters. The similarity distance is utilized to distinguish between clusters to identify the quality of clusters, where the value of similarity between (-1,1). Moreover, the cluster with a value similarity of more than 0 is identified as a highquality cluster. The probability distance is used to re-evolve the instances of negative clusters to suitable positive clusters. This stage results in consolidated optimal clusters to overcome the problem of imbalances data in the dynamic network efficiently. Additionally, the standard classifiers, such as the Random Forest (RF), K Nearest Neighbours (KNN), Decision Trees (DT), and Naïve Bayes (NB) classifier are utilized to classify data network traffic. Finally, the ISCX VPN-nonVPN dataset remarks as a benchmark to evaluate the proposed solution. The experiment results show that the performance evaluation achieves higher accuracy 81.9% compared to the standard classifiers and related works. [ABSTRACT FROM AUTHOR]