Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

Abstract

Accurate network traffic classification at early stage is very important for 5G network applications. During the last few years, researchers endeavored hard to propose effective machine learning model for classification of Internet traffic applications at early stage with few packets. Nevertheless, this essential problem still needs to be studied profoundly to find out effective packet number as well as effective machine learning (ML) model. In this paper, we tried to solve the above-mentioned problem. For this purpose, five Internet traffic datasets are utilized. Initially, we extract packet size of 20 packets and then mutual information analysis is carried out to find out the mutual information of each packet onnflow type. Thereafter, we execute 10 well-known machine learning algorithms using crossover classification method. Two statistical analysis tests, Friedman and Wilcoxon pairwise tests, are applied for the experimental results. Moreover, we also apply the statistical tests for classifiers to find out effective ML classifier. Our experimental results show that 13–19 packets are the effective packet numbers for 5G IM WeChat application at early stage network traffic classification. We also find out effective ML classifier, where Random Forest ML classifier is effective classifier at early stage Internet traffic classification.

A machine learning approach for feature selection traffic classification using security analysis

“…In our previous works [15][16][17], we applied ML algorithms in flow-based identification for the classification of IM applications, we achieved high promising accuracy results and improved the performance of the utilized ML algorithms. Similarly, several studies in the past [8,[18][19][20][21][22][23][24] have also applied ML algorithms for flow-based traffic classification, bandwidth management and security analysis.…”

Effective Feature Selection for 5G IM Applications Traffic Classification

“…We select 50 statistical flow-based features and got very effective accuracy results. Similarly, in [9,10], we classify IM traffic accurately and find out effective features for IM traffic classification using machine learning algorithms. However, more than 50 features increase computational complexity and decrease accuracy results.…”

“…In information theory, mutual information is extensively used for features selection [9,34], image processing [35], speech recognition [36], and so forth. It measures the mutual dependency between two random variables and , which describes the amount of information held by random variable.…”

“…calculate weight value [ ] for each features; (4) end for (5) for = 1 to ; (6) calculate WMI( ); 7if (WMI( ) > ); (8) insert into descending order; (9) end if (10) end for (11) = getfirstfeatures(list); (12) variable. So, by using the three equations in (6), the weighted mutual information can be obtained as follows:…”

“…(i) Wilcoxon test: we also used Wilcoxon signed-rank statistical test in this research. Wilcoxon test is also a nonparametric method used for pairwise comparison between two methods [40] and is also used in many research areas [9]. If is the variance between two methods' performance scores on th out of problem and the score is in different ranges, then it can be normalized on interval 0 and 1 in [41].…”