论文标题
密集IIOT的动态数据聚类中的传播控制反对虚假数据注射攻击
Dissemination Control in Dynamic Data Clustering For Dense IIoT Against False Data Injection Attack
论文作者
论文摘要
物联网已经使越来越多的驱动服务（例如工业IIOT服务）的开发经常涉及大量数据。同时，随着IIOT网络的增长，威胁甚至更大，错误的数据注入攻击（FDI）是最具侵略性的攻击之一。当前处理此攻击的当前解决方案没有考虑到数据验证，尤其是在数据群集服务上。旨在推进这一问题，这项工作引入了Coldinit，这是一种用于减轻对FDI攻击在密集IIOT网络中执行数据传播服务的入侵检测系统。 Condinit结合了监督监视和协作共识策略，以肯定地排除了各种外国直接投资攻击。模拟表明，与DDFC相比，在气压IIOT环境中，限量增加了35％-40％的群集数量。在多个IIT方案中，Coldinit的攻击检测率为99％，准确性为90，F1得分为0.81，仅为虚假负数和积极率的3.2％和3.6％。此外，在FDI攻击的两种变体下，Coldinit的检测率达到100％，精度为99和F1的0.93的准确性，较少2％的假阳性和负率。
The IoT has made possible the development of increasingly driven services, like industrial IIoT services, that often deal with massive amounts of data. Meantime, as IIoT networks grow, the threats are even greater, and false data injection attacks (FDI) stand out as being one of the most aggressive. The majority of current solutions to handle this attack do not take into account the data validation, especially on the data clustering service. Aiming to advance on the issue, this work introduces CONFINIT, an intrusion detection system for mitigating FDI attacks on the data dissemination service performing in dense IIoT networks. CONFINIT combines watchdog surveillance and collaborative consensus strategies for assertively excluding various FDI attacks. The simulations showed that CONFINIT compared to DDFC increased by up to 35% - 40% the number of clusters without attackers in a gas pressure IIoT environment. CONFINIT achieved attack detection rates of 99%, accuracy of 90 and F1 score of 0.81 in multiple IIoT scenarios, with only up to 3.2% and 3.6% of false negatives and positives rates, respectively. Moreover, under two variants of FDI attacks, called Churn and Sensitive attacks, CONFINIT achieved detection rates of 100%, accuracy of 99 and F1 of 0.93 with less than 2% of false positives and negatives rates.
