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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 datainjection attacks (FDI) stand out as being one of the most aggressive. Themajority of current solutions to handle this attack do not take into accountthe data validation, especially on the data clustering service. Aiming toadvance on the issue, this work introduces CONFINIT, an intrusion detectionsystem for mitigating FDI attacks on the data dissemination service performingin dense IIoT networks. CONFINIT combines watchdog surveillance andcollaborative consensus strategies for assertively excluding various FDIattacks. The simulations showed that CONFINIT compared to DDFC increased by upto 35% - 40% the number of clusters without attackers in a gas pressure IIoTenvironment. CONFINIT achieved attack detection rates of 99%, accuracy of 90and 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 twovariants of FDI attacks, called Churn and Sensitive attacks, CONFINIT achieveddetection rates of 100%, accuracy of 99 and F1 of 0.93 with less than 2% offalse positives and negatives rates.

Dissemination Control in Dynamic Data Clustering For Dense IIoT Against False Data Injection Attack