Processing and classifying IP packet data on the Internet based on machine learning
Các tập tin
Ngày
2024
Tác giả
Vuong, Xuan Chi
Nguyen, Kim Quoc
Tên Tạp chí
Tạp chí ISSN
Nhan đề tập
Nhà xuất bản
Nguyen Tat Thanh University
Giấy phép
Tóm tắt
Nowadays, the continuous development of information technology, communication over the Internet is increasing rapidly, and network congestion has become an alarming issue. To develop communication network infrastructure in a large city, a country, or globally, streamlining and controlling network data flow to optimize communication processes and minimize network congestion is crucial and necessary. In this study, the authors analyze and process data according to the delay of Internet Protocol (IP) packets, using machine learning models with the Random Forest (RF) and the Support Vector Machines (SVM) method to classify IP packets. The primary goal of classifying packets by delay is to optimize network performance by prioritizing processing of low-delay packets, ensuring stable and uninterrupted online services such as video streaming and voice calls. Furthermore, it is easy to manage and control packet traffic, hence minimizing network congestion at the router.
Mô tả
11 p.
Từ khóa
IP packet classification , IP network , Network congestion , Machine learning , Random forest , Mạng IP
Trích dẫn
Nguyen Tat Thanh University. (2024). Journal of Science and Technology - NTTU, Volume 7, Issue 2. ISSN 2615-9015.