Application for Traffic Classification Using Machine Learning Algorithms
Evgenii Nazarenko, Vitalii Varkentin, Aleksey Minbaleev · 2020
The timely detection of malicious traffic and the prevention of DDoS attacks is an important task in today's informational environment. There was considered a theoretical part of the classical machine learning algorithms (the principles of their work and the information about parameters). The Functional and non-functional requirements were identified then the action schemes and the use cases were presented. There was developed a traffic classification system appearing as a window application. There was presented the implementation of the classical machine learning algorithms and a user's interface. The developed network traffic identification and classification system uses the machine learning algorithms: k-NN, Naive Bayes, SVM, Ridge / Lasso, Decision Tree and k-Means. The accuracy of the system ranged from 46.69% to 99.81% depending on which algorithm was used.