Machine Learning based Network Packet Classification

Abstract

Network packet classification plays an important role in modern networks irrespective of host or network-based classification, serving as the foundation for efficient routing, malicious activity detection, and security enforcement. With the continuous growth of network traffic volume and complexity, traditional static rule-based classification methods have faced difficulties in scalability and adaptability. As a solution, the study decided to enforce machine learning techniques to tackle these challenges effectively. This study presents an extensive and original review of machine learning- based approaches for network packet classification. The smart Intrusion Detection System framework with network packet classification evolution looks forward to designing and deploying security systems that use various parameters for analysing current and dynamic traffic trends and are highly time-efficient in predicting intrusions. Various machine learning algorithms commonly employed in packet classification, such as decision trees, support vector machines, and neural networks are analysed and their merits and demerits are compared with their behaviour and accuracy percentage in this study. machine learning-based techniques offer an efficient and accurate network packet classification for the protection of the systems when compared to the conventional methods of packet classification. By leveraging the power of machine learning algorithms and intelligent feature selection, network administrators and Security Operation Center (SOC) analyst can enhance network performance, improve security, and the robustness of the log generated in the network.