Securing IoT-Based Smart Healthcare Systems by Using Advanced Lightweight Privacy-Preserving Authentication Scheme

Das, Sangjukta; Namasudra, Suyel; Deb, Suman; Ger, Pablo Moreno; Crespo, Ruben Gonzalez

doi:10.1109/jiot.2023.3283347

Cited by 15 publications

(4 citation statements)

References 39 publications

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“…To prevent medical information from being disclosed to unauthorized entities, healthcare users and IoMT devices must be authenticated in SHSs [139]. Smart healthcare systems employ authentication procedures combining ownership, knowledge, and biometric factors to strengthen authentication [99] and implement authentication-based techniques such as usernames and passwords, biometric authentication, two-factor and multifactor authentication, smart cards and tokens, digital certificates, one-time passwords, risk-based authentication, certificate-based authentication, behavioral authentication, client-based user authentication, contextual-based access control, RFID authentication, location-based authentication, blockchain-based authentication, role-based access control, and advanced lightweight privacy-preserving authentication schemes to allow patients and healthcare providers to efficiently establish secure communications to healthcare systems and IoMT devices and ensure robust security [47][138][161] [162]. Batista et al [99] reported that wearables may authenticate the identities of healthcare users by collecting user-centric data such as heart rate, body temperature, electrocardiogram signals, and body motions.…”

Section: Authentication-based Techniquesmentioning

confidence: 99%

Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles

Ali,

M. Mijwil

2024

Mesopotamian Journal of CyberSecurity

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Cutting-edge technologies have been widely employed in healthcare delivery, resulting in transformative advances and promising enhanced patient care, operational efficiency, and resource usage. However, the proliferation of networked devices and data-driven systems has created new cybersecurity threats that jeopardize the integrity, confidentiality, and availability of critical healthcare data. This review paper offers a comprehensive evaluation of the current state of cybersecurity in the context of smart healthcare, presenting a structured taxonomy of its existing cyber threats, mechanisms and essential roles. This study explored cybersecurity and smart healthcare systems (SHSs). It identified and discussed the most pressing cyber threats and attacks that SHSs face, including fake base stations, medjacking, and Sybil attacks. This study examined the security measures deployed to combat cyber threats and attacks in SHSs. These measures include cryptographic-based techniques, digital watermarking, digital steganography, and many others. Patient data protection, the prevention of data breaches, and the maintenance of SHS integrity and availability are some of the roles of cybersecurity in ensuring sustainable smart healthcare. The long-term viability of smart healthcare depends on the constant assessment of cyber risks that harm healthcare providers, patients, and professionals. This review aims to inform policymakers, healthcare practitioners, and technology stakeholders about the critical imperatives and best practices for fostering a secure and resilient smart healthcare ecosystem by synthesizing insights from multidisciplinary perspectives, such as cybersecurity, healthcare management, and sustainability research. Understanding the most recent cybersecurity measures is critical for controlling escalating cyber threats and attacks on SHSs and networks and encouraging intelligent healthcare delivery.

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Section: Authentication-based Techniquesmentioning

confidence: 99%

Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles

Ali,

M. Mijwil

2024

Mesopotamian Journal of CyberSecurity

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“…This efficiency can result in faster performance, reduced hardware costs, and lower energy consumption. For instance, in a recent work [44], authors have proposed a lightweight authentication system for IoT devices. The proposed system consumes fewer resources, like memory and CPU.…”

Section: Hybrid Model (Cnn With Lstm)mentioning

confidence: 99%

DeepMetaDroid: Real-Time Android Malware Detection Using Deep Learning and Metadata Features

Rahima Manzil,

Naik S

2024

Cloud Computing and Data Science

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The increasing prevalence of Android malware poses significant risks to mobile devices and user privacy. The traditional detection methods have limitations in keeping up with the evolving landscape of malware attacks, necessitating the development of more effective solutions. In this paper, we present DeepMetaDroid, a real-time detection approach for Android malware that leverages metadata features. By analyzing crucial metadata, including APK size, download size, permissions, certificates, and DEX files, the proposed method enables effective identification of malware and enhances mobile security. Using deep learning techniques, a lightweight Android real-time monitoring system is equipped with the trained model. These methods include long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and other ensemble models. Utilizing the rectified linear unit (ReLU) as the activation function, the DNN model is constructed with 32 neurons in the input layer. A one-dimensional convolutional layer with 32 neurons and a filter size of three is used as the input layer in the CNN model. The LSTM model is designed with an input layer consisting of 16 neurons. The GRU model with 32 neurons is employed in the input layer. Additionally, ensemble models that combined several architectures were developed. The proposed method offers a faster and more scalable solution for malware detection by consuming fewer resources like memory and CPU. This work ensures device security by providing real-time monitoring on Android devices to prevent users from installing malicious applications and, thus, enhance user privacy and security.

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“…Despite all the benefits of cloud computing, there are some major concerns like security [2], energy consumption, and resource optimization that could affect the efficiency and reliability of this cutting-edge technology [3,4]. Some researchers attempted to resolve these concerns by using virtual machine (VM) migration [5,6]. A VM migration service is defined as the process of migrating a virtual machine (VM) from one host computer to another in order to improve resource utilization, enhance load balancing, and reduce power consumption [7,8].…”

Section: Introductionmentioning

confidence: 99%

A secure VM Live migration technique in a cloud computing environment using blowfish and blockchain technology

Gupta,

Namasudra,

Kumar

2024

Preprint

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Data centers have proven to be the infrastructure's backbone to deliver cloud services. With the emerging paradigm of cloud computing, VM live migration is the process of migrating a running virtual machine across specific hosts with no client-visible interruption. Security, vulnerability, resource optimization, and maintaining the quality of service are many issues in live VM migration. Maintaining security in VM live migration is one of the important concerns. For creating a secure environment, this paper proposes a secure live migration technique by applying one of the cryptographic algorithms that are blowfish for generating an encryption-decryption-based system, and blockchain technology which provides a solution to address many challenges like decentralization, data privacy, and VM security to prevent from side-channel attack, and a man in the middle attacks. The algorithms namely Key Management Blowfish Encryption (KMBE), Access Control Searchable Encryption (ACSE), Protected Searchable Destination Server (PSDS), and Key Expansion Blowfish Decryption (KEBD) improve security in VM live migration in terms of various parameters like data center request servicing time, response time and data transfer cost. The proposed technique KMBE improves migration cost ($) by 60–70%, ACSE reduces overall energy consumption by 70–80%, PSDS reduces make span by 40–50% and KEBD improves the security in live VM migration by 30–40%.

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Securing IoT-Based Smart Healthcare Systems by Using Advanced Lightweight Privacy-Preserving Authentication Scheme

Cited by 15 publications

References 39 publications

Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles

Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles

DeepMetaDroid: Real-Time Android Malware Detection Using Deep Learning and Metadata Features

A secure VM Live migration technique in a cloud computing environment using blowfish and blockchain technology

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