基于GPU的分布式边缘路由器实时的超级恶意检测
GPU based Real-time Super Hosts Detection at Distributed Edge Routers
摘要：
超级主机是网络上的一种特殊主机，在一定的时间窗口内与许多其他主机联系。超级主机在扫描器检测、资源分配、垃圾邮件过滤等网络研究中发挥着重要作用，而如何实时的发现超级主机是这些应用的基础。在本文中，一种新的算法，表示为CBAA，提出了解决这个问题的边缘路由器。CBAA将网络流量划分为不同的部分。设计了一个比特立方体阵列来存储主机扫描数据包时不同业务部分的连接信息。在每个时间窗口结束时，CBAA非常快地恢复超级主机，因为每个流量部分中只有一小部分超级主机。CBAA也是一种并行算法。在GPU中部署CBAA可以很容易地实时的处理高速网络流量。在真实核心网络上的实验证明了我们算法的优势。
英文摘要：
The super host is a special host on the network which contacts with many other hosts during a certain time window. They play important roles in network researches such as scanners detection, resource allocation, spam filtering and so on. How to find super hosts in real time is the foundation of these applications. In this paper, a novel algorithm, denoted as CBAA, is proposed to solve this problem at edge routers. CBAA divides network traffic into different parts. A cube of bits array is devised to store hosts' linking information of different traffic parts when scanning packets. At the end of each time window, CBAA restores super hosts very fast because there are only a fraction of super hosts in each traffic part. CBAA is also a parallel algorithm. It's easy to deploy CBAA in GPU to deal with high-speed network traffic in real time. Experiments on a real-world core network prove the advantage of our algorithm.
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。
