Controle da Disseminação em Agrupamentos Dinâmicos de Dados Para Rede IoT Densa Contra o Ataque de Injeção de Dados Falsos
Abstract
The growth of IoT has made possible the creation of increasingly personalized services, that often deal with massive amounts of data. However, as IoT grows, its threats are even greater. Among the threats to dense IoTs, the false data injection attacks (FDI) stand out as being one of the most aggressive, especially on the service of data clustering. This dissertation proposes an intrusion detection mechanism, called CONFINIT, against FDI attacks on the data dissemination service in IoT dense. It combines strategies of watchdog surveillance and collaborative consensus for the detection of attackers, guaranteeing the authenticity of the data collected by th devices. An evaluation on the NS-3 simulator has shown that CONFINIT achieves detection rate of 99% for IDF attackers, an average accuracy of 0.84, and low rates of false positive and negatives. In addition, it has increased the capacity of clustering by up to 30%, proving its effectiveness in supporting the availability of the service.
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