Systematic Literature Review on the Use of Trusted Execution Environments to Protect Cloud/Fog-Based Internet of Things Applications

Valadares, Dalton Cézane Gomes; Will, Newton C.; Caminha, Jean; Perkusich, Mirko; Perkusich, Angelo; Gorgônio, Kyller Costa

doi:10.1109/access.2021.3085524

Cited by 24 publications

(16 citation statements)

References 68 publications

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“…Numerous opinion mining approaches are used to extract the feelings hidden in the comments and reviews for a specific unlocked mobile. Additionally, a thorough analysis of sentiment classification methods is performed using the data set from mobile phone assessments [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. The study’s [ 24 ] findings provide a comparison of eight distinct classifiers based on the assessment parameters of accuracy, recall, precision, and F-measure.…”

Section: Results and Comparative Analysismentioning

confidence: 99%

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Kaushik

Bhardwaj²,

Dahiya³

et al. 2022

Sensors

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Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company with an online presence to detect customer sentiment from their reviews automatically. This research aims to present a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive or negative thoughts. A dataset containing reviews of 3150 users has been used in this research work. Initially, a word cloud of positive and negative reviews was plotted, which gave a lot of insight from the text data. After that, a deep learning model using a multinomial naive Bayesian classifier was built and trained using 80% of the dataset. Then the remaining 20% of the dataset was used to test the model. The proposed model gives 93% accuracy. The proposed model has also been compared with four models used in the same domain, outperforming three.

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Section: Results and Comparative Analysismentioning

confidence: 99%

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Kaushik

Bhardwaj²,

Dahiya³

et al. 2022

Sensors

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“…Furthermore, the correct code must be executed in IoT scenarios where processing is already done within the IoT device (e.g., in automotive or healthcare applications). TEEs can be used on the device to protect the execution code and increase trust by attesting correctness to the backend system (Pettersen et al, 2017;Valadares et al, 2021).…”

Section: Example Applications Of Teementioning

confidence: 99%

Trusted Execution Environments: Applications and Organizational Challenges

Geppert

Deml²,

Sturzenegger³

et al. 2022

Front. Comput. Sci.

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A lack of trust in the providers is still a major barrier to cloud computing adoption – especially when sensitive data is involved. While current privacy-enhancing technologies, such as homomorphic encryption, can increase security, they come with a considerable performance overhead. As an alternative Trusted Executing Environment (TEE) provides trust guarantees for code execution in the cloud similar to transport layer security for data transport or advanced encryption standard algorithms for data storage. Cloud infrastructure providers like Amazon, Google, and Microsoft introduced TEEs as part of their infrastructure offerings. This review will shed light on the different technological options of TEEs, as well as give insight into organizational issues regarding their usage.

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“…To the best our knowledge, the IoT security in recent years is based on two ways [43], machine learning [23,29,31,34,39] or trusted execution environments [47,48,51]. These approaches often utilize a central server to control resource-constrained IoT devices outside of them.…”

Section: Related Workmentioning

confidence: 99%

IoT-REX: A Secure Remote-Control System for IoT Devices from Centralized Multi-Designated Verifier Signatures

Watanabe¹,

Yanai²,

Shikata³

2022

Preprint

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IoT technology has been developing rapidly, while at the same time, notorious IoT malware such as Mirai is a severe and inherent threat. We believe it is essential to consider systems that enable us to remotely control infected devices in order to prevent or limit malicious behaviors of infected devices. In this paper, we design a promising candidate for such remote-control systems, called IoT-REX (REmote-Control System for IoT devices). IoT-REX allows a systems manager to designate an arbitrary subset of all IoT devices in the system and every device can confirm whether or not the device itself was designated; if so, the device executes a command given from the systems manager. Towards realizing IoT-REX, we introduce a novel cryptographic primitive called centralized multi-designated verifier signatures (CMDVS). Although CMDVS works under a restricted condition compared to conventional MDVS, it is sufficient for realizing IoT-REX. We provide an efficient CMDVS construction from any approximate membership query structures and digital signatures, yielding compact communication sizes and efficient verification procedures for IoT-REX. We then discuss the feasibility of IoT-REX through cryptographic implementation of the CMDVS construction on a Raspberry Pi. Our promising results demonstrate that the CMDVS construction can compress communication size to about 30% and thus its resulting IoT-REX becomes three times faster than a trivial construction over typical low-power wide area networks with an IoT device. It is expected that IoT-REX can control 12,000 devices within a second.

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Systematic Literature Review on the Use of Trusted Execution Environments to Protect Cloud/Fog-Based Internet of Things Applications

Cited by 24 publications

References 68 publications

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Trusted Execution Environments: Applications and Organizational Challenges

IoT-REX: A Secure Remote-Control System for IoT Devices from Centralized Multi-Designated Verifier Signatures

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