NPC: Network Packet Classification Using Machine Learning Methodologies for Preventing Cyberattacks
Atul Kumar, Ishu Sharma · 2023
In modern networking, one of the most important tasks is network packet classification. This is done to ensure effective information routing, quality of service, and enforcement of security. The goal of this research is to improve the accuracy and efficiency of network packet classification by utilizing machine learning approaches as a strategy for thwarting cyberattacks. The Random Forest model is found to be the most successful of the several machine learning models that have been investigated. In this paper, we offer an overview of the difficulties engaged with networking packet classification, explore the application of machine learning approaches, and highlight the benefits of employing the Random Forest model. We show that the Random Forest model is superior to other models in terms of its precision, effectiveness, and robustness in preventing cyberattacks by conducting experimental evaluations and comparing it to other models. The results of this study provide important new insights into the ways in which machine learning might be able to improve network security by introducing new approaches to packet classification.