Published Paper Details:

State-of-the-art networks generate a huge amount of company data channels. Labeling these dates is essential for people of color, plus support for network tapes and cyber security scanning. There is an urgent need for data logic styles, which can perform online network data processing according to the emergence of new model dates. Online Machine Learning (OL) promises to support a similar type of data analysis. In this document, we examine and compare the LO methods that facilitate the analysis of data blocks in the web domain. We also examine the importance of business data analysis and highlight the benefits of internet literacy in this context and the challenges in analyzing the business block of a network based on OL technology, e.g. drifts and unbalanced classes. Let's look at the data flow processing tools and frameworks that can be used for online or on-the-fly processing of this data with its advantages and disadvantages and its inerrability into the de facto system data processing framework. To test the performance of OL techniques, we empirically evaluate the performance of various tree-based and ensemble-based network traffic classification algorithms. Finally, presented the open questions and future directions of traffic data flow analysis. This technical study provides valuable information and insight to the network research community when it comes to meeting requirements and the goals of online data flow analysis and network domain learning.