Research Area:  Mobile Ad Hoc Networks
In the process of science and technology development, although the control object is becoming more and more complex, the requirements for control performance are increasing. The contradiction of traditional control theory in FCMA algorithm is increasingly prominent in practical application, especially in robot control research and learning control, which is in urgent need of new vitality. At this time, a new control mode in the field of control, namely intelligent control, arises at the historic moment, which is a leap forward of control mode. In recent decades, the rapid development of the Internet and the development of a large scale, to avoid the belt to a variety of network intrusion, these network intrusion almost every moment. Network intrusion detection is an important way of computer security detection. It can identify the flow of external intrusion in the network, and carry out early warning processing. The network intrusion detection based on FCMA transforms the intrusion detection into the classification of network traffic, establishes a network traffic classification model, and classifies the network traffic by using machine learning classification standards, and detects the external intrusion traffic. Based on FCMA algorithm, this paper explores the method of network intrusion identification, and focuses on the data imbalance processing and feature selection in the process of network intrusion identification.
Author(s) Name:  Hanguang Li
Journal name:  Journal of Ambient Intelligence and Humanized Computing
Publisher name:  Springer
Paper Link:   https://link.springer.com/article/10.1007/s12652-021-03145-5