How Secure Having IoT Devices in Our Homes?

Estrada, Debora; Tawalbeh, Lo’ai; Vinaja, Robert

doi:10.4236/jis.2020.112005

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Besides, users are instructed to update security patches regularly. The users also taught and requested to avoid spam emails, third-party applications, or sources, which can compromise IoT security [25].…”

Section: Future Of the Internet Of Thingsmentioning

confidence: 99%

IoT Privacy and Security: Challenges and Solutions

et al. 2020

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Privacy and security are among the significant challenges of the Internet of Things (IoT). Improper device updates, lack of efficient and robust security protocols, user unawareness, and famous active device monitoring are among the challenges that IoT is facing. In this work, we are exploring the background of IoT systems and security measures, and identifying (a) different security and privacy issues, (b) approaches used to secure the components of IoT-based environments and systems, (c) existing security solutions, and (d) the best privacy models necessary and suitable for different layers of IoT driven applications. In this work, we proposed a new IoT layered model: generic and stretched with the privacy and security components and layers identification. The proposed cloud/edge supported IoT system is implemented and evaluated. The lower layer represented by the IoT nodes generated from the Amazon Web Service (AWS) as Virtual Machines. The middle layer (edge) implemented as a Raspberry Pi 4 hardware kit with support of the Greengrass Edge Environment in AWS. We used the cloud-enabled IoT environment in AWS to implement the top layer (the cloud). The security protocols and critical management sessions were between each of these layers to ensure the privacy of the users’ information. We implemented security certificates to allow data transfer between the layers of the proposed cloud/edge enabled IoT model. Not only is the proposed system model eliminating possible security vulnerabilities, but it also can be used along with the best security techniques to countermeasure the cybersecurity threats facing each one of the layers; cloud, edge, and IoT.

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Section: Future Of the Internet Of Thingsmentioning

confidence: 99%

IoT Privacy and Security: Challenges and Solutions

et al. 2020

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“…Face Recognition Using AI. The face recognition using deep learning-based AI implementation is inspired by Project Gurukul [32]. The implementation of the algorithm helps to identify the human face in a live video.…”

Section: 4mentioning

confidence: 99%

Artificial Intelligence-Powered Contactless Face Recognition Technique for Internet of Things Access for Smart Mobility

Hiremani

Hasan

Basavaraju

et al. 2022

Wireless Communications and Mobile Computing

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A contactless system became necessary for smart mobility during the COVID-19 pandemic. There are many touchpoints in private and public areas where contact is essential, such as intelligent transportation systems for vaccine carriers, patient ambulances, elevators, metros, buses, hospitals, and banks. A secured contactless device reduces the chances of COVID-19 infection spread. Several devices use smart cards, fingerprint identification, or code-based access. Most of these devices require some form of touch. The cost of such devices varies, depending on their capability and intended use. Sensors developed by using artificial intelligence (AI) to provide secured access are an emerging area. This paper presents an AI-powered contactless face recognition system. The solution has the Internet of Things (IoT) enabled access system. To identify a person, it uses AI assistance for face recognition with the help of Python Dlib’s facial recognition network. Dlib offers a wide range of functionality across several machine learning sectors and is open-source. The Arduino Uno (ATmega328P) and STK500 protocol has been used for communication to testify and validate the performance of the proposed technique. The objective is to detect and recognize faces by the proposed contactless approach. The obtained result shows 92% accuracy, 94% sensitivity, 96% precision and FRR 6% for face detection. There is a significant improvement in FRR in our work compared to the published 27.27%. The implemented solution in this paper provides accurate and secure contactless access to conventional, readily available techniques in public health safety.

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Secured smart mobile app for smart home environment

Latha

Soujanya

2021

Materials Today: Proceedings

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How Secure Having IoT Devices in Our Homes?

Cited by 7 publications

References 14 publications

IoT Privacy and Security: Challenges and Solutions

IoT Privacy and Security: Challenges and Solutions

Artificial Intelligence-Powered Contactless Face Recognition Technique for Internet of Things Access for Smart Mobility

Secured smart mobile app for smart home environment

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