Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML

Rasool, Raihan Ur; Ahmad, Hafiz Farooq; Rafique, Wajid; Qayyum, Adnan; Qadir, Junaid

doi:10.1016/j.jnca.2022.103332

Cited by 81 publications

(22 citation statements)

References 180 publications

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“…The DICOM information is frequently totally erased or transformed to some other standard, such as NIFTI (Neuroimaging Informatics Technology Initiative), which simply keeps track of voxel size and participant position, when sharing radiology images in open-source scientific projects. While eliminating all DICOM metadata from open-source scientific projects addresses privacy concerns, doing so lowers the quality of the information because metadata is essential for the creation of AI algorithms [ 349 ].…”

Section: Workflowmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

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Section: Workflowmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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“…This could be lethal for vulnerable individuals in society. 231 Traceability is a key component of intelligent predictions where the efficiency of predictions becomes inadequate to solve the overall problem, and the consequences behind the predictions need to be acknowledged. It makes smart city services questionable by avoiding predictions based on protected attributes such as gender, race, and age.…”

Section: Ethical Issues In Smart City Services Developmentmentioning

confidence: 99%

“…These attacks could bring devastating consequences, such as attacks on intelligent healthcare services that might lead to malicious hardware operations, causing disease diagnosis and treatment anomalies. This could be lethal for vulnerable individuals in society 231 . Traceability is a key component of intelligent predictions where the efficiency of predictions becomes inadequate to solve the overall problem, and the consequences behind the predictions need to be acknowledged.…”

Section: Current Challenges and Future Research Directionsmentioning

confidence: 99%

Developing smart city services using intent‐aware recommendation systems: A survey

Rafique

Hafid

Qadir

2023

Trans Emerging Tel Tech

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Smart cities could be defined as urban areas that use Information and Communication Technology (ICT) to solve city problems in efficient and sustainable ways. Intent-aware Recommender Systems (IARS) within ICT play a crucial role in filtering useless information according to user demands and assist in decision-making in various smart city platforms. In smart cities, the user traces on IoT, RFIDs, mobiles, and smart sensors capture actual user intent of performing an activity and enhance user satisfaction by proposing optimal services. This paper presents a detailed literature survey of the field of IARS and how it can be used for developing smart city services. First, we present the evolution of IARS with the development of computing technology. Then, we present case studies, synergies, advances, and a reference implementation architecture of IARS for smart cities. We discuss requirements for developing smart city services using IARS. Furthermore, we devise a comprehensive taxonomy of applications and techniques of IARS using different performance parameters. Finally, we elaborate on current issues, challenges, and future research directions in IARS; these directions we believe will pave the way for autonomous service provisioning in smart cities.

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“…Such malevolent activities can put the patient’s life at risk. Rasool et al [ 15 ] reviewed various security issues of IoMT devices. The authors describe the vulnerabilities that exist in these devices, which can be exploited by attackers easily.…”

Section: Related Workmentioning

confidence: 99%

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System

Zubair

Ghubaish

Ünal

et al. 2022

Sensors

Self Cite

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Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores.

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Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML

Cited by 81 publications

References 180 publications

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Developing smart city services using intent‐aware recommendation systems: A survey

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System

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