基于人工智能的智能通信数据分析

Intelligent Communication Data Analysis Based on Artificial Intelligence

对基于循环神经网络(Recurrent Neural Networks,RNN)的通信数据异常进行了深入分析,旨在提高通信网络的安全性和稳定性。首先,介绍了RNN的基本原理和应用,强调其在处理序列数据中的重要性。其次,应用RNN结构,给出了通信数据异常的分析方法,包括数据预处理、特征工程、模型构建、训练以及评估等。同时,利用NSL-KDD数据集进行实验验证,深入探讨异常检测的性能评估方法,包括精确度、召回率和F1分数。最后,对实验结果进行了分析。该方法在不同入侵类型和正常连接上具有较好的性能,为通信网络的安全性提供了有力支持。

In this paper,the communication data anomalies based on Recurrent Neural Networks(RNN)are deeply analyzed in order to improve the security and stability of communication networks.Firstly,the basic principle and application of RNN are introduced,and its importance in processing sequence data is emphasized.Secondly,using RNN structure,the analysis method of communication data anomaly is given,including data preprocessing,feature engineering,model construction,training and evaluation.At the same time,the NSL-KDD data set is used for experimental verification,and the performance evaluation methods of anomaly detection are discussed in depth,including accuracy,recall rate and F1 score.Finally,the experimental results are analyzed.This method has good performance in different intrusion types and normal connections,which provides strong support for the security of communication networks.