Wireless Networks

, Volume 25, Issue 4, pp 1533–1556 | Cite as

IoT6Sec: reliability model for Internet of Things security focused on anomalous measurements identification with energy analysis

  • Norisvaldo Ferraz JuniorEmail author
  • Anderson Silva
  • Adilson Guelfi
  • Sergio Takeo Kofuji


Wireless sensor and actuator devices with direct IPv6 Internet access with no human interaction compose the IP-connected Internet of Things (IoT). These devices are resource constrained in processing, memory, and energy—battery operated. IoT devices can have various applications. Although, when directly connected to the Internet they are susceptible to threats (e.g., malicious tamper of packet content to reduce the reliability of device data, the flood of requisitions for the devices to drain their energy). In this way, the literature shows the use of end-to-end security to provide confidentiality, authenticity, and integrity of IoT devices data. However, even with the benefit of secure IoT data, they are not enough to ensure reliable measurements. For this reason, this work presents a reliability model for IoT, focused on the identification of anomalous measurements (using multivariate statistics). In the experiments, we use spatial (proximity) and temporal (time interval variation) correlation, and datasets with true and false data. Additionally, we use an end-to-end secure scenario and analysis of energy consumption. The results prove the feasibility of the triad: reliability (within a system that identifies the type of the anomalous measurements), security, and low energy consumption.


IoT security Reliability system Anomalous measurements identification Energy analysis 



This work is funded by the Huawei Company—project number: 2994. The project is managed by Foundation of Support to the University of São Paulo (FUSP) and Eletronic Systems Department of University of São Paulo (PSI). Number of Company / Institution Agreement: OTABRA09160202003286840274.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Instituto de Pesquisas Tecnológicas - IPTSão PauloBrazil
  2. 2.Faculdade de Informática de Presidente Prudente - FIPPUniversidade do Oeste Paulista - UNOESTEPresidente Prudente, São PauloBrazil
  3. 3.Universidade Paulista - UNIPSão PauloBrazil
  4. 4.Escola PolitécnicaUniversidade de São Paulo - USPSão PauloBrazil

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