Treffer: Investigations on CFS-S feature selection algorithm-based network traffic intrusion detection.

The exponential growth trend of network traffic in the 5G era is caused by the rapid development of big data, cloud computing, cloud storage, and other technologies, which in turn cause an increase in network bandwidth. The purpose of Network Intrusion Detection System (NIDS) is to effectively identify various malicious behaviors by analyzing the acquired traffic data, so as to protect the network from attacks A network traffic intrusion detection system needs to dynamically collect and evaluate traffic data in order to guarantee the security of information. Still, a significant portion of the features in the collected traffic data are duplicates. Thus, the intrusion detection system's speed can be increased by minimizing and choosing the right features. This study fully merges the benefits of the conventional feature selection techniques, such as the quick filtering process and the high wrapping precision, by enhancing the CFS algorithm to create a hybrid feature selection strategy based on the CFS-S Algorithm. [ABSTRACT FROM AUTHOR]