Utilizing Machine Learning Techniques to Classify Network Traffic

Abstract

Within community of internet, it may be essential to recognize whatever programs are flowing via the networks in order to execute particular activities. Network traffic categorization is primarily used by Internet service providers (ISPs) to determine the qualities needed to construct a connection, which in turn influences the cable network current effectiveness. Stream, bandwidth, and machine-learning methods were all used to categorise internet protocol, and each has its own benefits and drawbacks. Because of its widespread use across disciplines as well as the increasing awareness between many investigators of its methodology when especially in comparison to everyone else, the Machine learning method [5-9] is popular these times. Naive Bayes as well as K-nearest algorithm results are then compared in this research whenever applied to a networking given dataset taken through live stream feeds using Ethernet program. Python's sklearn module and the pandas and numpy arrays modules are utilised as assist modules to create a machine learning algorithm. Our findings show that K closest approach is more efficient than Nave Bayes, Decision Tree, and Support Vector Machine algorithms.