Search Results (298)

The increasing reliance on cloud services demands advanced security mechanisms to protect sensitive data and ensure robust access control. This study addresses critical challenges in cloud security by proposing a novel framework that integrates blockchain-based smart contracts to enhance authorization and authentication processes. Smart contracts, as self-executing agreements embedded with predefined rules, enable decentralized, transparent, and tamper-proof mechanisms for managing access control in cloud environments. The proposed system mitigates prevalent threats such as unauthorized access, data breaches, and identity theft through an immutable and auditable security framework. A prototype system, developed using Ethereum blockchain and Solidity programming, demonstrates the feasibility and effectiveness of the approach. Rigorous evaluations reveal significant improvements in key metrics: security, with a 0% success rate for unauthorized access attempts; scalability, maintaining low response times for up to 100 concurrent users; and usability, with an average user satisfaction rating of 4.4 out of 5. These findings establish the efficacy of smart contract-based solutions in addressing critical vulnerabilities in cloud services while maintaining operational efficiency. The study underscores the transformative potential of blockchain and smart contracts in revolutionizing cloud security practices. Future research will focus on optimizing the system’s scalability for higher user loads and integrating advanced features such as adaptive authentication and anomaly detection for enhanced resilience across diverse cloud platforms. Full article

The implementation of the trans-European tourist cycle route network “EuroVelo” is fostering new strategic importance for non-motorized mobility and the associated practice of cycling tourism. Indeed, slow tourism offers a pathway for the development of inland areas. The infrastructure supporting it, such as long-distance cycling and walking paths, can act as a vital connection, stimulating regeneration in peripheral territories by enhancing environmental and landscape assets, as well as preserving heritage, local identity, and culture. The regeneration of peri-urban landscapes through soft mobility is recognized as the cornerstone for accessibility to material and immaterial resources (including ecosystem services) for multiple categories of users, including the most vulnerable, especially following the restoration of green-area systems and non-urbanized areas with degraded ecosystems. Considering the forthcoming implementation of the Magna Grecia cycling route, the southernmost segment of the “EuroVelo” network traversing three regions in southern Italy, this contribution briefly examines the necessity of defining new development policies to effectively integrate sustainable slow tourism with the enhancement of environmental and landscape values in the coastal areas along the route. Specifically, this case study focuses on a coastal stretch characterized by significant morphological and environmental features and notable landscapes interwoven with densely built environments. In this area, environmental and landscape values face considerable threats from scattered, irregular, low-density settlements, abandoned sites, and other inappropriate constructions along the coastline. Full article

HIV disproportionately affects key populations (MSM, transgender people, sex workers and psychoactive substance users), who face greater social vulnerability and limited healthcare access. This study aimed at analyzing sexual orientation and gender identity and their association with sexual practices, sexually transmitted infections and psychoactive substance use among PrEP users. Method: A cross-sectional study was conducted between January 2018 and June 2021 with 736 Brazilian PrEP users from a health service in São Paulo. Sociodemographic data, sexual behaviors, STI history (past 3 months) and psychoactive substances use (past 3 months) were extracted from clinical records. The associations were analyzed using binomial logistic regression (p < 0.05). Results: Most of the participants were cisgender men (93.4%) and homosexual (84.8%), with a mean age of 34.9 years old. Condomless sex was reported by 98.5%, and 18.4% had some recent sexually transmitted infection, mainly syphilis. Psychoactive substance use was reported by 55.4%, especially marijuana, club drugs, erectile stimulants and poppers. Transgender and cisgender women were more likely to report sex work and crack use. Homosexual and bisexual participants had higher odds of using erectile stimulants. Conclusions: The study reveals key links between gender, sexual orientation and risk behaviors, highlighting the need for inclusive, targeted prevention. Full article

Visual impairment or even loss seriously affects quality of life. Benefited by the rapid development of sound/laser detection, Global Positioning System (GPS)/Beidou positioning, machine vision and other technologies, the quality of life of blind people is expected to be improved through visual substitution technology. The existing visual substitution devices still have limitations in terms of safety, robustness, and ease of operation. The remote companion system developed here fully utilizes multimodal navigation and remote communication technologies, and the positioning and interaction functions of commercial mobile phones. Together with the accumulated judgment of backend personnel, it can provide real-time, safe, and reliable navigation services for blind people, helping them complete daily activities such as independent travel, circulation, and shopping. The practical results show that the system not only has strong operability and is easy to use, but also can provide users with a strong sense of security and companionship, making it suitable for promotion. In the future, this system can also be promoted for other vulnerable groups such as the elderly. Full article

Tourism offers many economic benefits but can have long-lasting ecological effects when improperly managed. Tourism can cause overwhelming pressure on wastewater treatment systems, as in Belize, where some of the over 400 small islands (cayes) that were once temporary sites for fishermen have become popular tourist destinations. An overabundance of nitrogen, in part as a result of incomplete wastewater treatment, threatens human health and ecosystem services. The tourism industry is a complex and dynamic industry with many sectors and stakeholders with conflicting goals. In this study, a systems thinking approach was adopted to study the dynamic interactions between stakeholders and the environment at Laughing Bird Caye National Park in Belize. The project centered on nutrient discharges from the caye’s onsite wastewater treatment system. An archetype analysis approach was applied to frame potential solutions to nutrient pollution and understand potential behaviors over time. “Out of control” and “Underachievement” were identified as system archetypes; “Shifting the Burden” and ‘‘Limits to Success’’ were used to model specific cases. Based on these results, upgrading of the wastewater treatment system should be performed concurrently with investments in the user experience of the toilets, education on the vulnerability of the treatment system and ecosystem, and controls on the number of daily tourists. Full article

Fog computing technology grants computing and storage resources to nearby IoT devices, enabling a fast response and ensuring data locality. Thus, fog-enabled IoT environments provide real-time and convenient services to users in healthcare, agriculture, and road traffic monitoring. However, messages are exchanged on public channels, which can be targeted to various security attacks. Hence, secure authentication protocols are critical for reliable fog-enabled IoT services. In 2024, Harbi et al. proposed a remote user authentication protocol for fog-enabled IoT environments. They claimed that their protocol can resist various security attacks and ensure session key secrecy. Unfortunately, we have identified several vulnerabilities in their protocol, including to insider, denial of service (DoS), and stolen verifier attacks. We also prove that their protocol does not ensure user untraceability and that it has an authentication problem. To address the security problems of their protocol, we propose a security-enhanced blockchain-based secure authentication protocol for fog-enabled IoT environments. We demonstrate the security robustness of the proposed protocol via informal and formal analyses, including Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. Moreover, we compare the proposed protocol with related protocols to demonstrate the excellence of the proposed protocol in terms of efficiency and security. Finally, we conduct simulations using NS-3 to verify its real-world applicability. Full article

Older adults make up about 12% of the public sector, primary care, and hospital use and represent a large proportion of the users of healthcare services. Older people are also more vulnerable to serious injury from unexpected falls due to tripping, slipping, or illness. This underscores the immediate necessity of stable and cost-effective e-health technologies in maintaining independent living. Artificial intelligence (AI) and machine learning (ML) offer promising solutions for early fall prediction and continuous health monitoring. This paper introduces a novel cooperative AI model that forecasts the risk of future falls in the elderly based on behavioral and health abnormalities. Two AI models’ predictions are combined to produce accurate predictions: The ${AI}_1$ model is based on vital signs using Fuzzy Logic, and the ${AI}_2$ model is based on Activities of Daily Living (ADLs) using a Deep Belief Network (DBN). A meta-model then combines the outputs to generate a total fall risk prediction. The results show 85.71% sensitivity, 100% specificity, and 90.00% prediction accuracy when compared to the Morse Falls Scale (MFS). This emphasizes how deep learning-based cooperative systems can improve well-being for older adults living alone, facilitate more precise fall risk assessment, and improve preventive care. Full article

This study presents a system for automatic cookie collection using bots that simulate user browsing behavior. Five bots were deployed, one for each of the most commonly used university browsers, enabling comprehensive data collection across multiple platforms. The infrastructure included an Ubuntu server with PiHole and Tshark services, facilitating cookie classification and association with third-party advertising and tracking networks. The BotSoul algorithm automated navigation, analyzing 440,000 URLs over 10.9 days with uninterrupted bot operation. The collected data established relationships between visited domains, generated cookies, and captured traffic, providing a solid foundation for security and privacy analysis. Machine learning models were developed to classify suspicious web domains and predict their vulnerability to XSS attacks. Additionally, clustering algorithms enabled user segmentation based on cookie data, identification of behavioral patterns, enhanced personalized web recommendations, and browsing experience optimization. The results highlight the system’s effectiveness in detecting security threats and improving navigation through adaptive recommendations. This research marks a significant advancement in web security and privacy, laying the groundwork for future improvements in protecting user information. Full article

With the increasing demand for drones in diverse tasks, the Internet of Drones (IoD) has recently emerged as a significant technology in academia and industry. The IoD environment enables various services, such as traffic and environmental monitoring, disaster situation management, and military operations. However, IoD communication is vulnerable to security threats due to the exchange of sensitive information over insecure public channels. Moreover, public key-based cryptographic schemes are impractical for communication with resource-constrained drones due to their limited computational capability and resource capacity. Therefore, a secure and lightweight key agreement scheme must be developed while considering the characteristics of the IoD environment. In 2024, Alzahrani proposed a secure key agreement protocol for securing the IoD environment. However, Alzahrani’s protocol suffers from high computational overhead due to its reliance on elliptic curve cryptography and is vulnerable to drone and mobile user impersonation attacks and session key disclosure attacks by eavesdropping on public-channel messages. Therefore, this work proposes a lightweight and security-enhanced key agreement scheme for the IoD environment to address the limitations of Alzahrani’s protocol. The proposed protocol employs a physical unclonable function and simple cryptographic operations (XOR and hash functions) to achieve high security and efficiency. This work demonstrates the security of the proposed protocol using informal security analysis. This work also conducted formal security analysis using the Real-or-Random (RoR) model, Burrows–Abadi–Needham (BAN) logic, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation to verify the proposed protocol’s session key security, mutual authentication ability, and resistance to replay and MITM attacks, respectively. Furthermore, this work demonstrates that the proposed protocol offers better performance and security by comparing the computational and communication costs and security features with those of relevant protocols. Full article

The increasing sophistication of web-based services has intensified the risk of zero-day attacks, exposing critical vulnerabilities in user information security. Traditional detection systems often rely on labeled attack data and struggle to identify novel threats without prior knowledge. This paper introduces a novel one-class ensemble method for detecting zero-day web attacks, combining the strengths of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and stacked autoencoders through latent representation concatenation and compression. Additionally, a structured tokenization strategy based on character-level analysis is employed to enhance input consistency and reduce feature dimensionality. The proposed method was evaluated using the CSIC 2012 dataset, achieving 97.58% accuracy, 97.52% recall, 99.76% specificity, and 99.99% precision, with a false positive rate of just 0.2%. Compared to conventional ensemble techniques like majority voting, our approach demonstrates superior anomaly detection performance by fusing diverse feature representations at the latent level rather than the output level. These results highlight the model’s effectiveness in accurately detecting unknown web attacks with low false positives, addressing major limitations of existing detection frameworks. Full article

With the advancement of communication technology, smart cities can provide remote services to users using mobile devices and Internet of Things (IoT) sensors in real time. However, the collected data in smart cities include sensitive personal information and data transmitted over public wireless channels, leaving the network vulnerable to security attacks. Thus, robust and secure authentication is critical to verify legitimate users and prevent malicious attacks. This paper reviews a recent authentication scheme for smart cities and identifies its susceptibilities to attacks, including insider attacks, sensor node capture, user impersonation, and random number leakage. We propose a secure and privacy-preserving authentication scheme for smart cities to resolve these security weaknesses. The scheme enables mutual authentication by incorporating biometric features to verify identity and using the physical unclonable function to prevent physical attacks. We evaluate the security of the proposed scheme via informal and formal analyses, including Burrows–Abadi–Needham logic, the real-or-random model, and the Automated Validation of Internet Security Protocols and Applications simulation tool. Finally, we compare the performance, demonstrating that the proposed scheme has better efficiency and security than existing schemes. Consequently, the proposed scheme is suitable for resource-constrained IoT-enabled smart cities. Full article

Financial technology (FinTech) has significantly changed access to financial services, particularly benefiting historically marginalized communities. While it offers many advantages, FinTech also brings substantial risks associated with this digital transformation. Recent studies highlight the significant impact of FinTech on financial inclusion, especially for marginalized populations. To investigate the benefits and drawbacks of FinTech and identify specific risks affecting users, particularly vulnerable groups, we employed the PRISMA method. A systematic literature review was conducted using the Web of Science database to explore recent research on FinTech and its relationship with financial inclusion, focusing on associated risks. The search covered 2010–2025; however, after applying inclusion criteria, the final dataset comprised publications from 2012 to 2025. Unlike previous bibliometric studies broadly addressing FinTech innovations, this review identifies and categorizes key risks affecting financial inclusion, emphasizing regulatory barriers, digital literacy, and socio-cultural challenges. The review is limited by the exclusive use of Web of Science and the English language, suggesting future research avenues using additional databases and multilingual sources. Findings reveal a notable increase in research activity surrounding FinTech and financial inclusion. This highlights challenges such as data privacy, regulation, and financial literacy. By mapping FinTech-related risks, this study aims to inform policymakers and stakeholders about effective strategies to mitigate these challenges and promote safe, inclusive financial ecosystems. Full article

The integration of electric vehicles into modern mobility ecosystems relies heavily on reliable charging station infrastructures that support real-time communications and data-driven functionalities. Existing solutions often face security vulnerabilities in their firmware update mechanisms, compromising safety, user trust, and the broader deployment of these stations in emerging digital and connected environments. This paper aims to address these gaps by proposing a blockchain-based framework designed to provide secure, tamper-proof firmware updates for charging stations in a Web of Things environment. The approach uses decentralized ledger technologies to validate firmware integrity, authenticate update sources, and mitigate the risk of malicious or fraudulent content. In a comprehensive experimental setup, the proposed method demonstrates enhanced resilience against unauthorized firmware modifications and improved traceability of update transactions through immutable records. Results highlight a reduction in firmware compromise events, as well as improved detection and notification efficiencies in real-time networked systems. These findings suggest that integrating blockchain technology into firmware update workflows strengthens security in electric vehicle charging infrastructures. Consequently, the adoption of decentralized verification approaches can drive broader trust in connected mobility services, supporting safer and more efficient charging station networks while fostering future innovation in sustainable transport. Full article

Many cities are developing semantic 3D models as digital representation of their environment to assess the outcomes of the implementation of new mobility services before their real-life piloting. So far, digital twins have reinforced evidence-based approaches for policy-making. The purpose of this paper is to present how digital twins can be used as a tool for vulnerable citizen engagement, improving efficiency of co-creation in mobility planning. An innovative methodological approach of integrating digital twins in engagement processes, by transforming them from real-time data-driven replicas, targeted to the facilitation of the decision-making process for the public authorities to a tool for enhancing citizen engagement in the mobility context has been developed and tested in Thessaloniki Smart Mobility Living Lab. Through a 3-steps workshops’ approach, the mobility needs set by vulnerable users are collected, prioritized and matched with policy plans. By combining citizens feedback with real-time data incorporated in digital twins, citizens receive back visualized scenarios of the impact of specific mobility measures on their neighborhoods. By lifting the citizen engagement in mobility planning to the next participatory level, the results of the study create a bridge between participatory processes and digital twins. Full article

Online video platforms have enabled unprecedented access to diverse content, but minors and other vulnerable viewers can also be exposed to highly graphic or violent materials. This study addresses the need for a nuanced method of filtering gore by developing a segmentation-based approach that selectively blurs blood. We recruited 37 participants to watch both blurred and unblurred versions of five gory video clips. Eye-based physiological and gaze data, including eye openness ratio, blink frequency, and eye fixations, were recorded via a webcam and eye tracker. Our results demonstrate that partial blood blurring substantially lowers perceived gore in more brutal scenes. Additionally, participants exhibited distinctive physiological reactions when viewing clips with higher gore, such as decreased eye openness and more frequent blinking. Notably, individuals with a stronger fear of blood showed an even greater tendency to blink, suggesting that personal sensitivities shape responses to graphic content. These findings highlight the potential of segmentation-based blurring as a balanced content moderation strategy, reducing distress without fully eliminating narrative details. By allowing users to remain informed while minimizing discomfort, this approach could prove valuable for video streaming services seeking to accommodate diverse viewer preferences and safeguard vulnerable audiences. Full article