Dynamic Anomalography：通过稀疏性和低秩跟踪网络异常
Dynamic Anomalography: Tracking Network Anomalies via Sparsity and Low Rank
摘要：
在大规模网络的骨干中，源到目的地（OD）业务流经历称为业务量异常的突然异常变化，这可能导致拥塞并限制满足最终用户服务质量要求的程度。为了在动态环境中保持无缝的终端用户体验，同时也为了保证网络安全，本文研究了一种称为动态异常图的关键网络监控任务。给定链路流量测量（未观察到的OD流的噪声叠加）定期获得的骨干路由器，目标是构建一个估计的异常实时的地图，从而总结了网络的“健康状态”沿流量和时间维度。利用OD流的低固有维数和异常的稀疏性，提出了一种新的在线估计的基础上，指数加权最小二乘准则与稀疏促进$\ell_1$ -范数的异常，和标称流量矩阵的核范数正则化。在将不可分离的核范数转换成适合在线优化的形式后，开发了一种动态异常图的实时算法，并在简化的技术假设下建立了其收敛性。对于操作条件下的计算复杂性减少是在溢价，一个轻量级的随机梯度算法的基础上Nesterov的加速技术，以及开发。综合的数值测试与合成和真实的网络数据证实了所提出的在线算法的有效性和跟踪能力，并证明他们优于国家的最先进的方法来诊断交通异常。
英文摘要：
In the backbone of large-scale networks, origin-to-destination (OD) traffic flows experience abrupt unusual changes known as traffic volume anomalies, which can result in congestion and limit the extent to which end-user quality of service requirements are met. As a means of maintaining seamless end-user experience in dynamic environments, as well as for ensuring network security, this paper deals with a crucial network monitoring task termed dynamic anomalography. Given link traffic measurements (noisy superpositions of unobserved OD flows) periodically acquired by backbone routers, the goal is to construct an estimated map of anomalies in real time, and thus summarize the network `health state' along both the flow and time dimensions. Leveraging the low intrinsic-dimensionality of OD flows and the sparse nature of anomalies, a novel online estimator is proposed based on an exponentially-weighted least-squares criterion regularized with the sparsity-promoting $\ell_1$-norm of the anomalies, and the nuclear norm of the nominal traffic matrix. After recasting the non-separable nuclear norm into a form amenable to online optimization, a real-time algorithm for dynamic anomalography is developed and its convergence established under simplifying technical assumptions. For operational conditions where computational complexity reductions are at a premium, a lightweight stochastic gradient algorithm based on Nesterov's acceleration technique is developed as well. Comprehensive numerical tests with both synthetic and real network data corroborate the effectiveness of the proposed online algorithms and their tracking capabilities, and demonstrate that they outperform state-of-the-art approaches developed to diagnose traffic anomalies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
