论文标题
通过分布式过滤协作和共识检测FDI对密集的物联网网络的攻击
Detecting FDI Attack on Dense IoT Network with Distributed Filtering Collaboration and Consensus
论文作者
论文摘要
物联网的兴起使得越来越多的个性化服务的开发，例如经常处理大量数据的工业服务。但是，随着物联网的增长，它的威胁甚至更大。错误的数据注入（FDI）攻击是对像物联网这样的数据网络最有害的攻击之一。处理此攻击的当前系统大多数没有考虑到数据验证，尤其是在数据群集服务上。这项工作引入了Condinit，这是一种反对性FDI攻击数据传播服务的入侵检测系统，成为密集的物联网。它结合了观察员的监视和在物联网设备之间共识，以迅速检测攻击者。在NS-3模拟器中评估了Condinit，将其用于致密的工业物联网，并获得了99％的检测率，占假阴性的3.2％和3.6％的假阳性率的3.6％，在没有外国直接投资攻击者的情况下总计35％。
The rise of IoT has made possible the development of %increasingly personalized services, like industrial services that often deal with massive amounts of data. However, as IoT grows, its threats are even greater. The false data injection (FDI) attack stands out as being one of the most harmful to data networks like IoT. The majority of current systems to handle this attack do not take into account the data validation, especially on the data clustering service. This work introduces CONFINIT, an intrusion detection system against FDI attacks on the data dissemination service into dense IoT. It combines watchdog surveillance and collaborative consensus among IoT devices for getting the swift detection of attackers. CONFINIT was evaluated in the NS-3 simulator into a dense industrial IoT and it has gotten detection rates of 99%, 3.2% of false negative and 3.6% of false positive rates, adding up to 35% in clustering without FDI attackers.
