摘要
文章提出一种基于机器学习的创新型方法,以提高通信网络入侵检测系统的检测效果。首先,深入研究了通信网络入侵检测的基本架构,以全面理解入侵行为的多样性和复杂性。其次,将正则化约束引入循环神经网络(Recurrent Neural Networks,RNN)模型,旨在提高检测准确性和模型的泛化能力。最后,利用UNSW-NB15数据集进行实验,证明所提方法的有效性。实验采用混淆矩阵进行结果分析,并通过精确度、召回率、F1分数等指标综合评估模型性能。结果表明,文章所提方法在通信网络入侵检测任务中表现出色,具有较高的准确性和泛化能力。
This paper proposes an innovative method based on machine learning to improve the detection effect of communication network intrusion detection system.Firstly,the basic architecture of communication network intrusion detection is deeply studied to fully understand the diversity and complexity of intrusion behavior.Secondly,regularization constraints are introduced into Recurrent Neural Networks(RNN)model to improve the detection accuracy and the generalization ability of the model.Finally,experiments are carried out on UNSW-NB15 data set to prove the effectiveness of the proposed method.In the experiment,the confusion matrix is used to analyze the results,and the performance of the model is comprehensively evaluated by indicators such as accuracy,recall and F1 score.The results show that the method proposed in this paper performs well in communication network intrusion detection tasks,and has high accuracy and generalization ability.
作者
罗卓君
LUO Zhuojun(Hunan Mass Media Vocational and Technical College,Changsha 410100,China)
出处
《通信电源技术》
2024年第3期128-130,共3页
Telecom Power Technology
关键词
机器学习
入侵检测
循环神经网络(RNN)
正则化约束
machine learning
intrusion detection
Recurrent Neural Network(RNN)
regularization constraint
