Correction to “ReViewNet: A Fast and Resource Optimized Network for Enabling Safe Autonomous Driving in Hazy Weather Conditions” [Jul 21 4256-4266]

Mehra, Aryan; Mandal, Murari; Narang, Pratik; Chamola, Vinay

doi:10.1109/tits.2020.3028669

Cited by 3 publications

(2 citation statements)

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“…Using Convolutional Neural Networks has been demonstrated to be significantly more effective than previous methods in identifying threats, surpassing techniques like Decision Trees, SVMs, and K-nearest neighbors (KNNs) [247]. In classification problems, CNNs are used as feedforward networks [248]. Essentially, Convolutional Neural Networks are characterized by two processes.…”

Section: F Convolutional Neural Network (Cnns) In Healthcare-iotmentioning

confidence: 99%

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Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

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Section: F Convolutional Neural Network (Cnns) In Healthcare-iotmentioning

confidence: 99%

“…In classification problems, CNNs are used as feed-forward networks [245]. Essentially, CNNs are characterized by two processes.…”

Section: F Convolutional Neural Network (Cnn) In Healthcare-iotmentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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Federated Learning for Computational Offloading and Resource Management of Vehicular Edge Computing in 6G-V2X Network

Hasan,

Jahan,

Nazri

et al. 2024

IEEE Trans. Consumer Electron.

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Automated Driving Safety Framework through Security Architecture - Survey

Geetha¹

2022

JTCSST

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An enormous wave of automation technology is about to hit the global market. To save thousands of people's lives, autonomous vehicle technology may decrease congestion and increase mobility, as well as may enhance the productivity of the transportation industry. Developed country governments, on the other hand, are concerned that they may be placing unneeded or unforeseen obstacles on the path of growth. As a result, when it comes to features, safety always comes first. With the help of various functions based on certain automation technologies, this effort attempts to find example ideas. A more technical look at the needs throughout the development to minimize safety-related dangers is presented in this paper, which is meant to augment previous publications on different safety elements. This article emphasizes the significance of safety by design. Additionally, the goal of this article is to overcome the problems of the existing system with authentication and security architecture framework.

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Correction to “ReViewNet: A Fast and Resource Optimized Network for Enabling Safe Autonomous Driving in Hazy Weather Conditions” [Jul 21 4256-4266]

Cited by 3 publications

References 1 publication

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Federated Learning for Computational Offloading and Resource Management of Vehicular Edge Computing in 6G-V2X Network

Automated Driving Safety Framework through Security Architecture - Survey

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