基于自回归滑动平均的网络数据流量预测模型

Research on Network Traffic Prediction Scheme Based on Autoregressive Moving Average

在无线网络中,对入侵攻击的准确和迅速的检测是关系到无线网络安全的重要问题。各种入侵攻击可以由其导致的网络流量的变化来检测。针对网络流量复杂的非线性以及混沌性,结合网络流量的时间序列特性,提出了一种基于自回归滑动平均(ARMA)的网络数据流量预测模型。该模型利用第三方检测系统,不需要耗费网络资源,能够迅速和准确地预测网络流量。采用从16个信道分析器获得的数据流量测量值对模型进行了初始化。仿真实验结果表明,文中提出的模型能够有效地检测网络入侵攻击,提高了整个网络的性能,延长了网络的寿命。

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems.Various types of intrusion attacks can be detected by the change in traffic flow that they induce.We proposed an intrusion detection system for WiA-PA networks.After modeling and analyzing traffic flow data by time-sequence techniques,we proposed a data traffic prediction model based on autoregressive moving average （ARMA） using the time series data.The model can quickly and precisely predict network traffic.We initialized the model with data traffic measurements taken by a 16-channel analyzer.Test results show that our scheme can effectively detect intrusion attacks,improve the overall network performance,and prolong the network lifetime.