高速物联网防火墙规则的实时动态配置

Yu-An Shao, C. Chao
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引用次数: 0

摘要

物联网(IoT)是现代社会不可或缺的一部分。由于它具有读取数据和连接系统的能力,最近进入了主流趋势。物联网网络平台包含各种应用,导致大量不同的网络流量涌入,黑客很容易发动大规模的网络攻击。黑客一旦控制了物联网设备,就可以通过摄像头、路由器等非常规计算设备发动大规模僵尸网络攻击。例如,2016年,域名系统提供商Dyn的物联网设备遭受了大规模的分布式拒绝服务攻击,导致Twitter和亚马逊等公司遭受了后果。因此,实时适应网络流量的大规模变化势在必行。防火墙是设备安全的基础。因此,当网络流量发生大规模变化时,需要保证防火墙的有效性,以降低攻击成功的概率。本研究提出了一种可以实时调整防火墙规则顺序的系统,用于高速物联网网络的流量监控。当系统检测到报文数量突然增加时,会立即重新排序并应用防火墙规则,以保证安全。同时保持了防火墙原有的过滤效果,不影响防火墙原有的过滤效果。测试结果表明,高速网络防火墙的性能有了明显的提高,过滤效果没有发现异常。
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Real-Time Dynamic Configuration of Firewall Rules for High-Speed IoT Networks
The Internet of Things (IoT) is indispensable to modern society. It has entered the mainstream trend recently owing to its ability to read data and connect systems. IoT network platforms comprise various applications, leading to an influx of heavy and varying network traffic, allowing hackers to launch large-scale network attacks easily. When hackers gain control of an IoT device, they can initiate large-scale botnet attacks even through nonconventional computing devices such as cameras and routers. For example, Dyn, a domain name system provider, experienced large-scale distributed denial-of-service attacks on its IoT devices in 2016, causing companies, such as Twitter and Amazon, to suffer the consequences. Therefore, adapting to large-scale changes in network traffic in real-time is imperative. Firewalls are the foundation of device security. Therefore, when large-scale changes in network traffic occur, it is necessary to ensure the effectiveness of firewalls to reduce the probability of successful attacks. This study proposes a system that can adjust the order of firewall rules in real-time to monitor the traffic in high-speed IoT networks. When the system detects a sudden increase in the number of packets, the firewall rules are reordered and applied immediately to ensure security. Additionally, the original filtering effect of the firewall is maintained without being compromised. The test results indicate that high-speed network firewall performance has improved significantly with no abnormality detected in the filtering effect.
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