To provide more reliable and secure Internet service, Internet service providers have more and more interests in network centric traffic analysis. This book considers this issue from two perspectives, which are of ISP's most interest: 1) network centric anomaly detection and 2) network centric traffic classification. In our study on network centric anomaly detection, we designed an edge router based framework to detect anomaly in the first place they enter network; we proposed the so-called two-way matching features, which are effective indicators of network anomalies; and we creatively considered spatial and temporal correlation among edge routers at the same time. To tap the potential profits made by multimedia services, ISPs are of much interest to detect voice and video traffic. Yet, to our best knowledge no existing approaches are available to separate between voice and video. To solve the problem, we creatively applied spectral analysis techniques to extract regularities in multimedia traffic and used minimum distance to subspace as classification metric. Results demonstrate the effectiveness and robustness of our approach.