人工智能下的网络流量预测与异常检测研究

Research on Network Traffic Prediction and Anomaly Detection under Artificial Intelligence

随着互联网和物联网的发展,网络流量预测与异常检测已成为保障网络安全的重要课题。传统的时间序列分析方法,如自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)和自回归滑动平均模型(Auto-Regressive Moving Average Model,ARMA),在复杂的网络环境中表现出了一定的局限性。为此,本文提出了一种基于人工智能的综合解决方案,结合回归分析和长短时记忆(Long Short-Term Memory,LSTM)网络进行流量预测,并利用自动编码器与随机森林进行异常检测。通过案例分析,该系统可提升网络流量预测准确性至96%,异常检测效率至95%。研究结果证明,人工智能综合方法在处理大规模网络数据中表现优异,为网络管理者提供了新的技术思路和实践路径。

With the development of the Internet and the Internet of Things,network traffic prediction and anomaly detection have become critical issues for ensuring network security.Traditional time series analysis methods,such as the autoregressive integrated moving average(ARIMA)and autoregressive moving average(ARMA)models,show certain limitations in complex network environments.Therefore,this paper proposes a comprehensive solution based on artificial intelligence,combining regression analysis and long short-term memory(LSTM)networks for traffic prediction,and utilizing autoencoders and Random Forests for anomaly detection.Through case studies,the system can improve network traffic prediction accuracy to 96%and anomaly detection efficiency to 95%.The research results demonstrate that the comprehensive AI-based approach performs exceptionally well in handling large-scale network data,offering new technical insights and practical pathways for network managers.