低分辨率小规模网络流量数据的混沌特性鉴别

使用非线性时间序列分析方法，对低分辨率采样的小规模网络流量数据进行混沌特性分析。给出了网络流量数据的平滑策略，计算了网络流量数据的最大Lyapunov指数，并对流量数据与噪声序列加以区分，从不同角度验证了小规模网络流量数据具有低维混沌特性。为采用混沌方法研究网络流量行为特性奠定了基础。

Differentiate Chaos Characters from Miniature Network Flow Data Sampled in Low Resolution

In this paper, chaos characters of miniature network flow data sampled in low resolution are differentiated, using methods of nonlinear time series analysis. Firstly, a smoothing method is given for network flow data. Then, largest Lyapunov exponent of flow data is computed, and noise data with the same characters are distinguished from network flow data. From different points of view, it proofs that network flow is a chaos system. These work provide the foundation for studying behavior characters of network flow using chaotic theory.