一种基于扩展自相似过程的VBR视频业务新模型

本文在扩展自相似过程的基础上 ,提出了一种简单的视频业务新模型。该模型的边缘分布近似Gamma分布 ,且其具有长或短的相关性。在许多时间尺度上 ,这些特性可以很好地匹配众多实际变比特率视频业务的一、二阶统计特征。仿真实验中 ,通过利用该模型模拟实际视频信号 ,我们证明它也能很好地反映实际视频业务的排队性能     

An adaptive network traffic prediction approach for LDoS attacks detection

Low‐rate denial of service (LDoS) attacks reduce throughput and degrade quality of service (QoS) of network services by sending out attack packets with relatively low average rate. LDoS attack flows are difficult to detect from normal traffic since it has the property of low average rate. The research on network traffic analysis and modeling shows that network traffic measurement data are irregular nonlinear time series. To characterize and analyze network traffic between attack and non‐attack situations, the adaptive normal and abnormal ν‐support vector regression (ν‐SVR) prediction models are constructed on the basis of the reconstructed phase space. In this paper, the dimension of reconstructed phase space for ν‐SVR is optimized by Bayesian information criteria method, and the parameter in the radial basis function is adaptively adjusted by minimizing the within‐class distance and maximizing the between‐class distance in the feature space. The nonthreshold decision function is obtained through calculating the prediction error of adaptive normal and abnormal ν‐SVR prediction models, which is adopted to detect LDoS attacks. Experiments in NS‐2 environment show that the adaptive ν‐SVR prediction model can effectively predict the network traffic measurement time series, and the probability distribution of time series generated by the adaptive ν‐SVR prediction model is quite similar to that of the network traffic measurement data. Experiments also clearly demonstrate the superiority of the proposed approach in LDoS attacks detection.     

Window-based rate control for video quality optimization with a novel INTER-dependent rate-distortion model

Most model-based rate control schemes use independent rate-distortion (R–D) models at macroblock (MB) level to represent the relationship among bit rate, distortion and encoding complexity. However the correlations between frames (INTER-dependency) are not well considered for distortion, bit allocation and quantization parameter (QP) decision. In this paper, a novel INTER-dependent R–D model is proposed based on the theoretical analysis of the relationship between the predicted residual of one frame and the distortion of its reference frame. To achieve both bit rate accuracy and consistent video quality, a window-based rate control scheme with two sliding windows is introduced. One window is to group certain previously encoded frames and current frame to control the bit rate and buffer delay; the other is to group certain future encoding frames to optimize the fluctuation of video quality. Furthermore, the optimization of Lagrange multiplier is also discussed under the INTER-dependent situation. Experimental results demonstrate that the proposed window-based rate control scheme with INTER-dependent R–D model can achieve accurate target bit rate and improve PSNR performance, meanwhile the variation of PSNR is the smallest compared with other three benchmark algorithms. This one-pass rate control scheme is highly practical for the real-time video coding applications.