CLASSIFICATION SCHEME OF NETFLOW DATA FOR NETWORK SECURITY USING MACHINE LEARNING

ABSTRACTS

Network security is a major concern in today's digital landscape, as firms face a constant barrage of cyber threats. To successfully defend their networks, organizations must design efficient and robust categorization algorithms capable of identifying hostile activity and unusual behavior. This study proposes a thorough classification strategy for NetFlow data that uses machine learning approaches to improve network security. NetFlow is a popular network protocol for capturing and recording network traffic data, which provides useful insights into network behavior. However, the sheer volume and complexity of NetFlow data make manual analysis difficult. By automating the classification process, machine learning technologies provide a viable solution for rapid detection and reaction to possible security incidents.Various machine learning algorithms, such as k-means clustering and neural networks, are used in the suggested categorization strategy. These methods were developed using unlabeled NetFlow data. The results show that the proposed classification approach detects a wide range of network security threats.This study advances network security by utilizing machine learning to provide an effective and scalable approach to NetFlow classification.