Internet of Things (IoT) resources cooperate with themselves for requesting and providing services. In heterogeneous and complex environments, those resources must trust each other. On-Off attacks threaten the IoT trust security through nodes performing good and bad behaviors randomly, to avoid being rated as a menace. Some countermeasures demand prior levels of trust knowledge and time to classify a node behavior. In some cases, a malfunctioning node can be mismatched as an attacker. In this paper, we introduce a smart trust management method, based on machine learning and an elastic slide window technique that automatically assesses the IoT resource trust, evaluating service provider attributes. In simulated and real-world data, this method was able to identify On-Off attackers and fault nodes with a precision up to 96% and low time consumption.

Trusted Execution Environments have been applied to improve data security in many distinct application scenarios since they enable data processing in a separate and protected region of memory. To investigate how this technology has been applied to the different IoT scenarios, which commonly deal with specific characteristics such as device resource constraints, we carried out a systematic literature review. For this, we selected and analyzed 58 papers from different conferences and journals, identifying the main IoT solutions and scenarios in which TEE has been employed. We also gathered the mentioned TEE advantages and disadvantages as well as the suggestions for future works. This study gives a general overview of the use of TEEs for cloud/fog-based IoT applications, bringing some challenges and directions.

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