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Article
Computer Science, Artificial Intelligence
Tanzeela Shakeel et al.
Summary: This research paper provides a recent review of current and future healthcare applications, security, market trends, and technology implementation based on the Internet of Medical Things (IoMT). It analyzes the progress of IoMT implementation in addressing various healthcare concerns and discusses potential obstacles and issues. The survey also offers a comprehensive overview of different disciplines of IoMT to empower future researchers in the field.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Automation & Control Systems
Fazlullah Khan et al.
Summary: The Internet of Medical Things (IoMT) effectively addresses various issues in conventional healthcare systems such as personnel shortages, care quality, insufficient supplies, and expenses. IoMT technology offers advantages in treatment efficiency and quality, but faces increasing cyberattacks. This article proposes a cyberattack detection method using ensemble learning and fog-cloud architecture to ensure security. The method outperforms baseline approaches in terms of precision by 4% according to evaluation on the ToN-IoT dataset.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Editorial Material
Computer Science, Theory & Methods
Andrzej Goscinski et al.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2023)
Article
Computer Science, Information Systems
Bushra Tahir et al.
IEEE Journal of Biomedical and Health Informatics
(2023)
Article
Mathematics
Sneha Jadhav et al.
Article
Construction & Building Technology
Rajesh Natarajan et al.
Summary: The Internet of Medical Things (IoMT) is a revolutionary method of connecting medical equipment and software to healthcare networks. It offers various cloud-based applications and uses wireless sensor networks for data gathering and delivery. A unique energy-efficient routing protocol called ECC-EERP is proposed to ensure high security and energy efficiency in healthcare 5.0.
Review
Computer Science, Artificial Intelligence
Ayoub Si-Ahmed et al.
Summary: The Internet of Medical Things (IoMT) has revolutionized the healthcare industry by enabling physiological data collection using sensors and transmitting them for analysis. It offers benefits like early disease detection and automatic medication, but also presents security risks like patient privacy violations. Therefore, adopting robust security measures is crucial to ensure data integrity and confidentiality.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Sharmin Aktar et al.
Summary: With the increase in the use and evolution of the cyber world, different network attacks, especially Distributed Denial-of-Service (DDoS) attacks, have become a significant threat to the Internet. This paper proposes a deep learning-based model using a contractive autoencoder to detect anomalies in network traffic and successfully achieves high accuracy in intrusion detection on different datasets. The results indicate the effectiveness of the proposed method in defending against network attacks.
COMPUTERS & SECURITY
(2023)
Article
Chemistry, Analytical
Mousa Alalhareth et al.
Summary: In healthcare, the Internet of Medical Things (IoMT) is used for remote patient monitoring and real-time diagnoses. However, there are cybersecurity threats that can compromise patient data and well-being. This paper proposes an enhanced feature selection technique, LRGU-MIFS, which outperforms existing techniques and overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns.
Review
Green & Sustainable Science & Technology
Bharat Bhushan et al.
Summary: Recent advancements in M2M communications, mini-hardware manufacturing, and micro computing have led to the development of IoT, which is integrated with medical devices to improve treatment, patient monitoring, and personalized healthcare. However, the development of more complex and heterogeneous IoMT systems with their own operating systems and protocols has resulted in security and privacy issues. This paper highlights the significance of implementing security measures in IoMT systems and presents techniques for securing data transmission, collection, and storage, as well as exploring future research directions in the field.
Article
Chemistry, Multidisciplinary
Stylianos Karagiannis et al.
Summary: This research introduces Chidroid, an innovative mobile Android application that retrieves, collects, and distributes logs from smart healthcare devices. It can parse non-structured data into structured data for machine learning and deep learning, and serve as a universal policy-based tool to analyze security issues in recent Android versions.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Sandesh Achar et al.
Summary: Cyber-physical security is crucial for safeguarding vital computing infrastructure from cyber attacks, which can cause significant losses in terms of data, finances, reputation, and more. Unlike software-level security, cyber-physical system security presents additional challenges as it involves physical inspection and monitoring. This paper introduces an innovative algorithm based on human activity recognition to strengthen cyber-physical security with minimal human intervention. The proposed algorithm utilizes machine vision and cloud server technology to achieve high accuracy in classifying suspicious activities, and it can be implemented at a cost of just USD 4.29 +/- 0.29 per month.
Article
Engineering, Multidisciplinary
Sohail Saif et al.
Summary: The internet of medical things is a popular application of IoT that involves connecting various small medical devices to share sensitive data. However, these IoT devices and networks are susceptible to security attacks, which can pose a major problem for time-critical applications like healthcare. To address this issue, machine learning and deep learning techniques can be applied to create an effective intelligent botnet attack detection engine (IBADE). This paper presents a performance analysis of several machine learning models to identify the most effective one for IBADE modeling.
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
(2023)
Review
Engineering, Multidisciplinary
Pritika Pritika et al.
Summary: The adaptation of the Internet of Medical Things (IoMT) has transformed the healthcare industry by providing efficient and timely services. However, the growing number of threats in the cyber world has raised concerns about security and privacy, particularly for personal and sensitive user data. Little attention has been paid to the methodologies used for the risk assessment of heterogeneous IoMT devices. This paper reviews the existing literature on the topic, analyzes available risk assessment frameworks, and highlights the need for new approaches to address the heterogeneity of the risks.
Article
Computer Science, Theory & Methods
Izhar Ahmed Khan et al.
Summary: The Internet of Medical Things (IoMT) is replacing traditional healthcare systems, with less focus on security. Explainable AI (XAI) helps improve trust level and enables security experts to understand predictive decisions.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Sudarshan Nandy et al.
Summary: The paper proposes an Empirical Intelligent Agent method based on Swarm-Neural network to identify attackers in the edge-centric Internet of Medical Things framework. Test results on the ToN-IoT dataset demonstrate that the proposed method achieves 99.5% accuracy in identifying attacks during data transmission.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Zewen Li et al.
Summary: This review provides insights into the development history of CNN, a overview of various convolutions, introduction to classic and advanced CNN models, conclusions drawn from experimental analysis, rules of thumb for function and hyperparameter selection, and applications of 1-D, 2-D, and multidimensional convolutions. Moreover, it also discusses open issues and promising directions for CNN as guidelines for future work.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Izhar Ahmed Khan et al.
Summary: The Industrial Internet of Things (IIoT) and Industry 4.0 enable interrelation among manufacturing processes, industrial machines, and utility services. However, the integrity and accuracy of data in IIoT networks can be compromised by malicious events, highlighting the need for effective anomaly detection and explanation methods to ensure quality services and data security.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Chemistry, Analytical
Ogobuchi Daniel Okey et al.
Summary: With the advancement and widespread use of Internet of Things (IoT) systems, ensuring the security of these systems has become crucial. This paper proposes an efficient method for detecting cyberattacks and network intrusions in IoT systems using boosted machine learning classifiers. Experimental results demonstrate the effectiveness of the proposed method.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Sandeep Trivedi et al.
Summary: This paper presents a method for brain tumor segmentation using deep learning, including a novel nested deep neural network and an innovative multimodality fusion network. The experimental results demonstrate that the proposed method achieves outstanding performance in segmenting various parts of brain tumors.
2022 IEEE 13TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON)
(2022)
Review
Computer Science, Information Systems
Neha Garg et al.
Summary: This article introduces the communication environment of the Internet of Medical Things (IoMT), and highlights the security requirements, potential security attacks and threats of IoMT. The article also provides the network model, adversary model, and taxonomy of security protocols for IoMT communication, as well as a comparative analysis of several security protocols. Additionally, the article suggests future research directions.
SECURITY AND PRIVACY
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Joseph Bamidele Awotunde et al.
Summary: The emergence of smart healthcare has enabled the use of medical sensors to create an intelligent Internet of Medical Things (IoMT) system. However, the transfer of patient's data to external devices for processing can pose security and privacy risks. This paper proposes a swarm-neural network-based model to detect intruders in the data-centric IoMT-based system. Experimental results show promising accuracy rates for the proposed model.
INFORMATICS AND INTELLIGENT APPLICATIONS
(2022)
Article
Business
Lean Yu et al.
Summary: Missing data has become a serious problem in credit risk classification and this study proposes a one-hot encoding-based data preprocessing method to solve this problem. The experimental results suggest that the proposed method performs the best with high missing rates and the random sample imputation method performs better with low missing rates.
EMERGING MARKETS FINANCE AND TRADE
(2022)
Article
Computer Science, Artificial Intelligence
Ankit Thakkar et al.
Summary: The main goal of network security is to protect information flow and sensitive data, with Intrusion Detection System (IDS) being a common security mechanism. Our research focuses on the impact of feature selection techniques on IDS performance, utilizing a variety of machine learning techniques for experimentation.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Business
Sriram Tarikere et al.
Summary: Although Internet of Medical Things (IoMT) technology is still in the early stages of adoption, cybersecurity is crucial for its development to prevent potential crises from privacy and security risks. This article discusses the market opportunities and risks associated with IoMT, as well as a plan for proactively mitigating concerns and promoting growth while maintaining consumer confidence in the healthcare system.
Article
Biology
Nuruzzaman Faruqui et al.
Summary: LungNet is a hybrid deep-convolutional neural network model trained with CT scan and MIoT data, achieving high accuracy in lung cancer diagnosis and outperforming traditional CNN classifiers.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Review
Neurosciences
Andrew Saxe et al.
Summary: Deep neural networks offer potential theories for perception, cognition, and action in biological brains, potentially reshaping our understanding of neural systems. This Perspective provides a roadmap for how neuroscientists can utilize deep networks to model and understand biological brains.
NATURE REVIEWS NEUROSCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Shuai Li et al.
Summary: This paper introduces the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to improve generalization performance by connecting with recent interest in spectrally regularized deep learning methods. Theoretical analyses and experiments demonstrate that OrthDNNs can achieve local isometric properties on practical data distributions, leading to better optimization of network weights. Proposed algorithms, including strict and approximate OrthDNNs, along with the SVB and BBN methods, show effective and efficient performance in benchmark image classification tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Review
Computer Science, Information Systems
Ali Ghubaish et al.
Summary: The advancements in Internet of Things (IoT) technologies have transformed the healthcare industry through remote patient monitoring, enhancing patient care efficiency. Yet, the security of these systems remains a major challenge, requiring robust measures to safeguard data.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Proceedings Paper
Computer Science, Information Systems
Arnaud Rosay et al.
Summary: As the number of connected devices increases, network intrusion detection becomes more crucial. Analysis of older datasets revealed issues, which newer datasets aim to address. CIC-IDS2017 dataset, with raw data and flow-based features, is widely used for network intrusion detection.
PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY (ICISSP)
(2021)
Article
Chemistry, Analytical
Javier Silvestre-Blanes et al.
Review
Chemistry, Analytical
Dimitris Koutras et al.
Editorial Material
Multidisciplinary Sciences
Samuel G. Finlayson et al.
Review
Computer Science, Artificial Intelligence
Yong Yu et al.
NEURAL COMPUTATION
(2019)
Article
Computer Science, Artificial Intelligence
Marco Lippi et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Article
Chemistry, Analytical
Manuel Lopez-Martin et al.
Article
Computer Science, Software Engineering
Gabriel Eilertsen et al.
ACM TRANSACTIONS ON GRAPHICS
(2017)
Article
Engineering, Electrical & Electronic
JW Hwang et al.
IEEE SIGNAL PROCESSING LETTERS
(2004)
