Internet of Things (IoT) has been deployed in a vast number of smart applications with the aim to bring ease and comfort into our lives. However, with the expansion of IoT applications, the number of security and privacy breaches has also increased, which brings into question the resilience of existing security and trust mechanisms. Furthermore, the contemporaneous centralized technology is posing significant challenges viz scalability, transparency and efficiency to wide range of IoT applications such as smart logistics, where millions of IoT devices need to be connected simultaneously. Alternatively, IOTA is a distributed ledger technology that offers resilient security and trust mechanisms and a decentralized architecture to overcome IoT impediments. IOTA has already been implemented in many applications and has clearly demonstrated its significance in real-world applications. Like any other technology, IOTA unfortunately also encounters security vulnerabilities. The purpose of this study is to explore and highlight security vulnerabilities of IOTA and simultaneously demonstrate the value of threat modeling in evaluating security vulnerabilities of distributed ledger technology. IOTA vulnerabilities are scrutinized in terms of feasibility and impact and we have also presented prevention techniques where applicable. To identify IOTA vulnerabilities, we have examined existing literature and online blogs. Literature available on this topic is very limited so far. As far as we know IOTA has barely been addressed in the traditional journals, conferences and books. In total we have identified six vulnerabilities. We used Common Vulnerability Scoring System (CVSS v3.0) to further categorize these vulnerabilities on the basis of their feasibility and impact.
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http://dx.doi.org/10.3390/s21051834 | DOI Listing |
Int J Health Plann Manage
January 2025
Community Health Impact Coalition, London, UK.
Community health workers (CHWs) are the backbone of strong primary healthcare systems. If properly supported, they can add significant value to access to healthcare service delivery. Yet, despite their proven effectiveness globally, systemwide support for CHWs remains sub-optimal.
View Article and Find Full Text PDFCureus
December 2024
Community Medicine, Dhanalakshmi Srinivasan Medical College and Hospital, Siruvachur, IND.
Background Women's psychological well-being (PWB) is influenced by various factors, including their occupational status and social roles. In India, where traditional and modern roles often intersect, understanding the PWB of homemakers and employed women is crucial for developing targeted mental health interventions. This study aimed to compare the overall and domain-specific PWB between homemakers and employed women in the Perambalur district, Tamil Nadu, using the 18-item Ryff's PWB Scale (PWBS).
View Article and Find Full Text PDFHeliyon
January 2025
School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China.
This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability.
View Article and Find Full Text PDFInt Arch Occup Environ Health
January 2025
Coordination for the Innovation and Application of Science and Technology (CIACYT), Autonomous University of San Luis Potosi, Sierra Leona Avenue No. 550, Lomas Second Section, San Luis Potosi, C.P. 78210, SLP, Mexico.
Purpose: Individuals in occupational environments are particularly susceptible to the impacts of pollutants; making it crucial to assess systemic inflammation markers. The study aimed to evaluate the immune response to inflammation through the assessment of a cytokine profile in individuals working in vulnerable conditions exposed to heavy metals.
Methods: A total of 137 adults participated in this study from three work environments: brickyards, waste scavenging and quarries.
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
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