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Cost optimization of secure routing with untrusted devices in software defined networking

机译:在软件定义网络中使用不受信任的设备的安全路由的成本优化

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Over the years, switches and network routers have been compromised frequently, and a lot of vulnerabilities have occurred in network infrastructure. Secure routing (SR) is one of the challenges that currently exists in computer networks. Software-defined networks (SDN) are designed by assuming that routers or switches are trustworthy. In SDN, untrusted devices have resulted in security issues such as traffic analysis, failure to receive information, packet change and removal, inaccurate routing, and network downtime. Using encryption methods is a possible solution to deal with some of these problems, but it requires additional infrastructure as well as significant overhead at runtime. One of the most trusted routing methods is through replicated devices (switches or routers). Recently we have seen less attention given to the number of replicated devices in SR. In this paper, the problem of SR is converted into a multi-objective optimization problem, considering the reliability of different manufacturers and SR with untrusted devices is performed. To this end, a mathematical model is provided to study the objectives of maximum reliability and cost minimization. The NSGA-Ⅱ algorithm is applied to determine the optimal number of replicated devices in order to minimize the cost of implementing SR in spite of the presence of untrusted devices in SDN. Our simulation results show that our proposed method compared to the base method (without considering optimization) decreases implementation cost by 27% and increases the reliability from 70% to 93.2%.
机译:多年来,交换机和网络路由器经常受到影响,网络基础设施中发生了许多漏洞。安全路由(SR)是计算机网络中当前存在的挑战之一。通过假设路由器或交换机可信赖,设计了软件定义的网络(SDN)。在SDN中,不受信任的设备导致了诸如流量分析之类的安全问题,未能接收信息,数据包更改和删除,不准确的路由和网络停机时间。使用加密方法是处理一些问题的可能解决方案,但它需要额外的基础架构以及运行时的大量开销。最受信任的路由方法之一是通过复制设备(交换机或路由器)。最近,我们已经看到SR中复制设备的数量较少。在本文中,考虑到不同制造商和SR的可靠性,将SR的问题转换为多目标优化问题。为此,提供了一种数学模型来研究最大可靠性和成本最小化的目标。施加NSGA-Ⅱ算法以确定复制设备的最佳数量,以便最小化实现SR的成本,尽管在SDN中存在不可信的设备。我们的仿真结果表明,我们的提出方法与基础方法(不考虑优化)相比减少了27%的实施成本,并将可靠性从70%增加到93.2%。

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