Network Security Education with Machine Learning Optimization
Xiaoyan Yao,Wei Liu
Information Security Education Journal, Vol. 11, No. 2, 2024.
DOI: https://doi.org/10.6025/isej/2024/11/2/37-43
Link: https://www.dline.info/isej/fulltext/v11n2/isejv11n2_1.pdf
Abstract:
The current network security environment is increasingly severe, and more than
static network protection mechanisms are needed to meet network security demands.
Dynamic intrusion detection methods can provide real-time protection and
detection for the network security environment. Therefore, this paper builds an
intrusion detection education based on the machine learning algorithm and random
forest and optimizes the model using the FSCA and TPE algorithms. Experimental
results show that the intrusion detection model in this study has higher detection
efficiency than other models and demonstrates strong adaptability, enabling rapid
identification of intrusion behaviors and timely response in different intrusion detection
environments, thereby improving the stability and practicality of the model.