Search Results (89)

Wireless Medical Sensor Networks (WMSNs) collect and transmit patients’ physiological data in real time through various sensors, playing an increasingly important role in intelligent healthcare. Authentication protocols in WMSNs ensure that users can securely access real-time data from sensor nodes. Although many researchers have proposed authentication schemes to resist common attacks, insufficient attention has been paid to insider attacks and ephemeral secret leakage (ESL) attacks. Moreover, existing adversary models still have limitations in accurately characterizing an attacker’s capabilities. To address these issues, this paper extends the traditional adversary model to better reflect practical deployment scenarios, assuming a semi-trusted server and allowing adversaries to obtain users’ temporary secrets. Based on this enhanced model, we design an efficient ECC-based authentication and key agreement protocol that ensures the confidentiality of users’ passwords, biometric data, and long-term private keys during the registration phase, thereby mitigating insider threats. The proposed protocol combines anonymous authentication and elliptic curve cryptography (ECC) key exchange to satisfy security requirements. Performance analysis demonstrates that the proposed protocol achieves lower computational and communication costs compared with existing schemes. Furthermore, the protocol’s security is formally proven under the Random Oracle (ROR) model and verified using the ProVerif tool, confirming its security and reliability. Therefore, the proposed protocol can be effectively applied to secure data transmission and user authentication in wireless medical sensor networks and other IoT environments. Full article

Pregnane X receptor (PXR) is an important nuclear receptor that regulates diverse physiological functions, including drug metabolism. Although PXR activation is potentially involved in adverse events, the full scope of its impact has yet to be elucidated. In this study, we developed a machine learning model to predict the activity of PXR agonists and applied the model to drugs listed in the US Food and Drug Administration Adverse Event Reporting System database. Analysis of the predicted agonist–active drug interactions and adverse event reports revealed statistically significant risks (lnROR > 1 and −logp > 1.3) for multiple cardiac disorders. These findings suggest that PXR activity is involved in cardiovascular adverse effects and may contribute to drug safety through the early identification of risks. Full article

The Industrial Internet of Things (IIoT), a key enabler of Industry 4.0, integrates advanced communication technologies with the industrial economy to enable intelligent manufacturing and interconnected systems. Secure and reliable identity authentication in the IIoT becomes essential as connectivity expands across devices, systems, and domains. Blockchain technology presents a promising solution due to its decentralized, tamper-resistant, and traceable characteristics, facilitating secure and transparent identity verification. However, current blockchain-based cross-domain authentication schemes often lack a lightweight design, rendering them unsuitable for latency-sensitive and resource-constrained industrial environments. This paper proposes a lightweight cross-domain authentication scheme that combines blockchain with Chebyshev chaotic mapping. Unlike existing schemes relying heavily on Elliptic Curve Cryptography or bilinear pairing, our design circumvents such computationally intensive primitives entirely through the algebraic structure of Chebyshev polynomials. A formal security analysis using the Real-Or-Random (ROR) model demonstrates the scheme’s robustness. Furthermore, performance evaluations conducted with Hyperledger Fabric and the MIRACL cryptographic library validate the method’s effectiveness and superiority over existing approaches in terms of both security and operational efficiency. Full article

The multi-factor authentication (MFA) protocol requires users to provide a combination of a password, a smart card and biometric data as verification factors to gain access to the services they need. In a single-server MFA system, users accessing multiple distinct servers must register separately for each server, manage multiple smart cards, and remember numerous passwords. In contrast, an MFA system designed for multi-server architecture allows users to register once at a registration center (RC) and then access all associated servers with a single smart card and one password. MFA with an offline RC addresses the computational bottleneck and single-point failure issues associated with the RC. In this paper, we propose a post-quantum secure MFA protocol for a multi-server architecture with an offline RC. Our MFA protocol utilizes the post-quantum secure Kyber key encapsulation mechanism and an information-theoretically secure fuzzy extractor as its building blocks. We formally prove the post-quantum semantic security of our MFA protocol under the real or random (ROR) model in the random oracle paradigm. Compared to related protocols, our protocol achieves higher efficiency and maintains reasonable communication overhead. Full article

Background: Depression, a major global health issue, is commonly treated with selective serotonin reuptake inhibitors (SSRIs). Given the link between depression and inflammation, nonsteroidal anti-inflammatory drugs (NSAIDs) may have adjunctive benefits. Clinically, SSRIs and NSAIDs are often co-prescribed for comorbid pain or inflammatory conditions. However, both drug classes pose risks of adverse effects, and their interaction may lead to clinically significant drug–drug interactions. Objectives: This study analyzed FDA Adverse Event Reporting System (FAERS) data (2004–2024) to assess gastrointestinal bleeding, thrombocytopenia, and acute kidney injury (AKI) potential risks linked to SSRIs (citalopram, escitalopram, fluoxetine, paroxetine, fluvoxamine, and sertraline) and NSAIDs (propionic/acetic/enolic acid derivatives, COX-2 inhibitors) in depression patients, alone and combined. Methods: Disproportionality analysis (crude reporting odds ratios, cROR) identified possible associations; drug interactions were evaluated using Ω shrinkage, additive, multiplicative, and combination risk ratio (CRR) models. Results: Gastrointestinal bleeding risk was potentially elevated with citalopram (cROR = 2.81), escitalopram (2.27), paroxetine (2.17), fluvoxamine (3.58), sertraline (1.69), and propionic acid NSAIDs (3.17). Thrombocytopenia showed a potential correlation with fluoxetine (2.11) and paroxetine (2.68). AKI risk may be increased with citalopram (1.39), escitalopram (1.36), fluvoxamine (3.24), and COX-2 inhibitors (2.24). DDI signal analysis suggested that citalopram in combination with propionic acid derivatives (additive model = 0.01, multiplicative model = 1.14, and CRR = 3.13) might increase the risk of bleeding. Paroxetine combined with NSAIDs (additive model = 0.014, multiplicative model = 2.65, and CRR = 2.99) could potentially increase the risk of thrombocytopenia. Sertraline combined with NSAIDs (Ω₀₂₅ = 0.94, multiplicative model = 2.14) might be associated with an increasing risk of AKI. Citalopram combined with propionic acid derivatives (Ω₀₂₅ = 1.08, multiplicative model = 2.17, and CRR = 2.42) could be associated with an increased risk of acute kidney injury. Conclusions: Certain combinations of SSRIs and NSAIDs might further elevate these risks of gastrointestinal bleeding, thrombocytopenia, and acute kidney injury in patients with depression. Given the potential drug–drug interactions, heightened clinical vigilance is advised when prescribing SSRIs and NSAIDs in combination to patients with depression. Full article

Smart farming is an agricultural technology integrating advanced technology such as cloud computing, Artificial Intelligence (AI), the Internet of Things (IoT), and robots into traditional farming. Smart farming can help farmers by increasing agricultural production and managing resources efficiently. However, malicious attackers can attempt security attacks because communication in smart farming is conducted via public channels. Therefore, an authentication scheme is necessary to ensure security in smart farming. In 2024, Rahaman et al. proposed a privacy-centric authentication scheme for smart farm monitoring. However, we demonstrated that their scheme is vulnerable to stolen mobile device, impersonation, and ephemeral secret leakage attacks. This paper suggests a secure and privacy-preserving scheme to resolve the security defects of the scheme proposed by Rahaman et al. We also verified the security of our scheme through “the Burrows-Abadi-Needham (BAN) logic”, “Real-or-Random (RoR) model”, and “Automated Validation of Internet Security Protocols and Application (AVISPA) tool”. Furthermore, a performance analysis of the proposed scheme compared with related studies was conducted. The comparison result proves that our scheme was more efficient and secure than related studies in the smart farming environment. Full article

Backgrounds/Objectives: This study aims to comprehensively characterize the prevalence and severity of antiepileptic drug (AED)-induced adverse drug events (ADEs) and to identify predictors strongly associated with serious adverse events (SAEs) in both general and geriatric populations. Methods: This cross-sectional study investigated AED-related ADEs reported to the KIDS KAERS DB from January 2014 to December 2023. Disproportionality analysis was performed to detect the association between reported SAEs, and multiple logistic regression was conducted to identify predictors associated with SAEs. Cox’s proportional hazard model was utilized to assess ADE duration in elderly patients aged 60 years and older. Results: More than 50% of 36,809 AED-related ADEs were reported in elderly patients aged 60 years and older, and the prevalence of SAEs was 3.78%. ADEs associated with endocrine disorders had the highest likelihood of SAEs being reported (ROR 15.30), followed by hematological disorders. The predictors associated with elevated SAE risks in the elderly were male sex (OR 1.91; 95% CI 1.62–2.27), aging (OR 1.17; 95% CI 1.04–1.31), and certain AEDs. However, the concomitant administration of acid-suppressive therapy (AST) and opioids was associated with a lower risk of SAEs in the elderly population. Elderly patients not receiving concomitant AST were less likely to experience prolonged ADE duration (HR 0.28, 95% CI 0.07–1.15); however, no substantial differences in ADE duration were observed with the concomitant use of opioids. Conclusions: This study implies significant variability in the frequency, severity, and duration of ADEs depending on the type of AEDs, patient demographics, and concomitant medication use. Full article

The conventional supply chain management has undergone major advancements following IoT-enabled revolution. The IoT-enabled drones in particular have ignited much recent attention for package delivery in logistics. The service delivery paradigm in logistics has seen a surge in drone-assisted package deliveries and tracking. There have been a lot of recent research proposals on various aspects of last-mile delivery systems for drones in particular. Although drones have largely changed the logistics landscape, there are many concerns regarding security and privacy posed to drones due to their open and vulnerable nature. The security and privacy of involved stakeholders needs to be preserved across the whole chain of Supply Chain Management (SCM) till delivery. Many earlier studies addressed this concern, however with efficiency limitations. We propose a Physical Uncloneable Function (PUF)-based secure authentication protocol (PSL-IoD) using symmetric key operations for reliable last-mile drone delivery in SCM. PSL-IoD ensures mutual authenticity, forward secrecy, and privacy for the stakeholders. Moreover, it is protected from machine learning attacks and drone-related physical capture threats due to embedded PUF installations along with secure design of the protocol. The PSL-IoD is formally analyzed through rigorous security assessments based on the Real-or-Random (RoR) model. The PSL-IoD supports 26.71% of enhanced security traits compared to other comparative studies. The performance evaluation metrics exhibit convincing findings in terms of efficient computation and communication along with enhanced security features, making it viable for practical implementations. 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

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

Background/Objectives: Regadenoson, a selective adenosine A_2A receptor agonist, is primarily prescribed for myocardial perfusion imaging (MPI). As its clinical use becomes more widespread in practice, assessing its safety in real-world settings is essential. Methods: In this research, disproportionality analysis was applied to evaluate the safety of Regadenoson by examining all adverse event (AE) reports since 2004 in the FDA Adverse Event Reporting System (FAERS), in which Regadenoson was identified as the primary suspected drug. The reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN) were used to analyze AEs associated with Regadenoson. The Weibull distribution was utilized to model the temporal risk of AEs. Results: The results confirmed some known adverse reactions, such as nausea, shortness of breath (dyspnea), palpitations/vomiting, headache, dizziness, chest pain, and flushing (facial redness or warmth), which were also listed on the drug’s label. New potential adverse reactions not mentioned in the label were identified, including micturition urgency, mental status changes, conversion disorder, eye movement disorder, and genital paraesthesia. This study highlighted the significance of monitoring AEs, particularly right after the start of Regadenoson administration. Conclusions: This study provides preliminary safety data on Regadenoson’s real-world use, corroborating known adverse effects while uncovering new potential risks. These findings offer valuable safety insights for clinicians when prescribing Regadenoson for the use of MPI. Full article

Internet of Vehicles (IoV) is a concept that combines IoT and vehicular ad hoc networks. In IoV environments, vehicles constantly move and communicate with other roadside units (edge servers). Due to the vehicles’ insufficient computing power, repetitive authentication procedures can be burdensome for automobiles. In recent years, numerous authentication protocols for IoV environments have been proposed. However, there is no study that considers both re-authentication and handover authentication situations, which are essential for seamless communication in vehicular networks. In this study, we propose a chaotic map-based seamless authentication scheme for edge-assisted IoV environments. We propose authentication protocols for initial, handover, and re-authentication situations and analyze the security of our scheme using informal methods, the real-or-random (RoR) model, and the Scyther tool. We also compare the proposed scheme with existing schemes and show that our scheme has superior performance and provides more security features. To our knowledge, This paper is the first attempt to design an authentication scheme considering both handover and re-authentication in the IoV environment. Full article

The Internet of Drones (IoD) is an emerging industry that offers convenient services for humans due to the high mobility and flexibility of drones. The IoD substantially enhances human life by enabling diverse drone applications across various domains. However, a malicious adversary can attempt security attacks because communication within an IoD environment is conducted through public channels and because drones are vulnerable to physical attacks. In 2023, Sharma et al. proposed a physical unclonable function (PUF)-based authentication and key agreement (AKA) scheme for the IoD. Regrettably, we discover that their scheme cannot prevent impersonation, stolen verifier, and ephemeral secret leakage (ESL) attacks. Moreover, Sharma et al.’s scheme cannot preserve user untraceability and anonymity. In this paper, we propose a secure and lightweight AKA scheme which addresses the shortcomings of Sharma et al.’s scheme. The proposed scheme has resistance against diverse security attacks, including physical capture attacks on drones, by leveraging a PUF. Furthermore, we utilize lightweight operations such as hash function and XOR operation to accommodate the computational constraints of drones. The security of the proposed scheme is rigorously verified, utilizing “Burrows–Abadi–Needham (BAN) logic”, “Real-or-Random (ROR) model”, “Automated Validation of Internet Security Protocols and Application (AVISPA)”, and informal analysis. Additionally, we compare the security properties, computational cost, communication cost, and energy consumption of the proposed scheme with other related works to evaluate performance. As a result, we determine that our scheme is efficient and well suited for the IoD. Full article

Background: Midazolam is widely used in clinical anesthesia, but its effects on the Bispectral Index (BIS) and propofol concentration at the effector site (CeP) are underexplored. This study investigates the pharmacodynamic interaction between midazolam and propofol in total intravenous anesthesia (TIVA) with target-controlled infusion (TCI), focusing on Schnider and Eleveld models. Methods: This prospective study included breast surgery patients receiving TIVA-TCI. BIS and CeP were assessed at loss of responsiveness (LoR), during maintenance (MA), and at return of responsiveness (RoR). Incidences of unwanted spontaneous responsiveness (USRE), burst suppression episodes (BSuppE), and postoperative delirium (POD) were recorded. Results: Midazolam premedication significantly reduced propofol doses and CeP at LoR and during MA, without affecting CeP at RoR. In the Schnider model, midazolam reduced total propofol dose, while in the Eleveld model, it lowered BIS at LoR. Unwanted anesthesia events occurred in 36.2% of patients, including USRE (10%), BSuppE (26.2%), and POD (1.2%). BSuppE rates were lower in the Schnider model and reduced in the midazolam group in the Eleveld model. Conclusions: Midazolam premedication influences CeP and BIS in TIVA-TCI, with model-specific variations, optimizing propofol management and improving patient outcomes. Full article

Background: Drug reaction with eosinophilia and systemic symptoms is a severe cutaneous reaction with a high mortality rate. It is challenging to diagnose due to its similar presentation to infectious disease syndromes, variation with the culprit drug, and lack of awareness. Methods: We searched PubMed, and Embase, for RegiSCAR-scored observational studies, the FDA Adverse Events Reporting System (FAERS) for adverse event reports, and the Allele Frequency Net Database (AFND) for HLA allele frequency. In our meta-analysis, we employed a random effects model to subgroup patients by ethnicity to determine the proportion of DRESS cases compared with various associated medications. Additionally, we identified a correlation between the proportion of cases and the presence of the HLA*A-32:01allele, which is suspected to predispose individuals to DRESS. Results: Twenty-one studies on 1949 DRESS cases in vancomycin and 2558 antimicrobial DRESS reports in the FAERS database were analyzed. Meta-analysis showed a 27% incidence of vancomycin-DRESS, with Caucasians having the highest proportion at 36%. The median latency for symptom onset was 21 days, with no female predisposition. The proportional incidence of vancomycin-DRESS did not correlate with the HLA-A*32:01 allele. The adjusted ROR for vancomycin was 2.40 compared to other antimicrobials, and the risk increased by 77% with concurrent antimicrobials. Piperacillin/tazobactam had a similar DRESS reporting risk at 0.95 (95%CI: 0.88–1.02). Conclusions: Vancomycin significantly contributes to the incidence of DRESS and is more closely related to ethnicity than to allele frequency, indicating that the HLA-A*32:01 allele may not be directly involved. Furthermore, the use of other antimicrobials can influence the reaction, underscoring the need to minimize antimicrobial use for better coverage. Full article