Security Analysis Research Articles

Formal Verification and Security Analysis of Go-based New Simple Queue System

Formal Verification and Security Analysis of Go-based New Simple Queue System

Retraction Note: Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models

Retraction Note: Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.

Retraction Note: Cyber security analysis based medical image encryption in cloud IoT network using quantum deep learning model

Retraction Note: Cyber security analysis based medical image encryption in cloud IoT network using quantum deep learning model

Improving the End‐to‐End Protection in E‐Voting Using BVM—Blockchain‐Based E‐Voting Mechanism

ABSTRACTVoting has always been a crucial topic of public attention for democratic reasons. The ease of use and low cost are the result of e‐voting being frequently used for such important decision outcomes. However, the tremendous authority and intervening data in current e‐voting systems make it risky and difficult to achieve correct equity and clarity in e‐voting. So, by combining e‐voting with blockchain technology, these issues can be resolved while providing reorganization and intervention‐resistant characteristics. A voter's improper manipulation, frequent voting, or non‐party voting, may also undermine fairness. A verifier is therefore required to check the e‐voting mechanism in order to ensure its effectiveness and control the process equality and fairness. In this paper, a Blockchain‐based e‐Voting Mechanism (BVM) is developed for providing the end to end security and fairness for transparent voting. This mechanism also provides a zero‐knowledge proof (ZP) based verifier to inspect the voting procedure against voter's mis‐operations and uses a novel Improved Master‐key Administration (IMA) based public key cryptography to attack prevention. The utilization of blockchain technology ensures transparency, anonymity, confidentiality, authentication, tamper resistance, and a high level of data integrity, making it a promising choice for modernizing and enhancing the electoral process. Also, the performance of BVM has been compared with similar voting mechanisms and analyzed based on time complexity, security analysis, performance factors like delay and throughput, and anti‐attack examination.

Retraction Note: Elastic optical network routing analysis using intelligent machine learning architecture with security analysis

Retraction Note: Elastic optical network routing analysis using intelligent machine learning architecture with security analysis

LightAuth: A Lightweight Sensor Nodes Authentication Framework for Smart Health System

ABSTRACTCounterfeit medical devices pose a threat to patient safety, necessitating a secure device authentication system for medical applications. Resource‐constrained sensory nodes are vulnerable to hacking, prompting the need for robust security measures. Token‐based authentication schemes, such as one‐time passwords (OTPs), smart cards, key fobs, and mobile authentication apps, along with certificate‐based authentication methods, such as client and code‐signing, employ cryptographic frameworks like elliptical curve cryptography (ECC) and physical unclonable functions (PUF). However, these methods face challenges, including block sequence issues and susceptibility to side‐channel attacks. To address these issues, we propose a framework for mutual authentication using private Ethereum. This framework integrates private Ethereum and cryptographic techniques for encrypting and decrypting data using mathematical algorithms to overcome block sequence issues and side‐channel attacks. Similarly, fog nodes are utilised to enhance local computing, storage, and networking capabilities for sensors. The framework is evaluated using metrics such as communication costs, execution costs, and computation costs based on Ethereum gas consumption. The performance of the LightAuth framework is compared with that of the Smart Contracts Against Counterfeit IoMT (SCACIoMT) framework, designed for Internet of Medical Things (IoMT) devices. The effectiveness of LightAuth is verified through formal security analysis using BAN logic.

Retraction Note: E-healthcare application cyber security analysis using quantum machine learning in malicious user detection

Retraction Note: E-healthcare application cyber security analysis using quantum machine learning in malicious user detection

An Anonymous Authenticated Key Agreement Scheme for Telecare Medical Information Systems

With the rapid development of information technology from one side and the experience of the COVID-19 pandemic from the other side, people presently prefer to access healthcare services remotely. Telecare Medical Information System (TMIS) provides more flexible, faster, and more convenient e-healthcare services available to all people, particularly those who lack access to physicians due to their geographical restrictions. However, due to the sensitivity of medical information, preventing unauthorized access to patient data and preserving patient privacy is crucial. In this paper, we propose an authenticated key agreement scheme for TMIS to preserve the privacy of the patient’s identity from all internal (even the health server and the physician) and external entities. Moreover, the physician’s identity is kept secret from all external entities. Formal and informal security analysis of the proposed scheme indicates that it is secure against all attacks in the context.

Efficient Quantum Private Comparison with Unitary Operations

Quantum private comparison (QPC) is a crucial component of quantum multiparty computing (QMPC), allowing parties to compare their private inputs while ensuring that no sensitive information is disclosed. Many existing QPC protocols that utilize Bell states encounter efficiency challenges. In this paper, we present a novel and efficient QPC protocol that capitalizes on the distinct characteristics of Bell states to enable secure comparisons. Our method transforms private inputs into unitary operations on shared Bell states, which are then returned to a third party to obtain the comparison results. This approach enhances efficiency and decreases the reliance on complex quantum resources. A single Bell state can compare two classical bits, achieving a qubit efficiency of 100%. We illustrate the feasibility of the protocol through a simulation on the IBM Quantum Cloud Platform. The security analysis confirms that our protocol is resistant to both eavesdropping and attacks from participants.

Ultrasound-guided percutaneous microwave ablation of primary hyperthyroidism: security and efficacy analysis

Objectives This study aimed to analyze the safety, efficacy, and application prospects of ultrasound-guided microwave ablation (MWA) in the treatment of primary hyperthyroidism. Methods Eight patients with primary hyperthyroidism who underwent ultrasound-guided glandular volume reduction between January 2021 and December 2022 were included in this study. Pre- and postablative examinations, including grayscale ultrasound, contrast-enhanced ultrasound (CEUS), laboratory examination, antithyroid drug (ATD) dosage, and quality of life (QoL) assessment via Thyroid-Specific Patient-Reported Outcome Short-Form (ThyPRO-39), were analyzed retrospectively. The main ultrasound follow-up times were 1, 3, 6, and 12 months after ablation. Thyroid function was reviewed 1 week after ablation, and the subsequent reexamination time was determined according to the patient’s specific test results and ATD adjustment. The final efficacy was comprehensively analyzed according to the patient’s medication status, thyroid function, thyroid volume, and QoL score. Results All 8 patients successfully received MWA for primary hyperthyroidism, and none of them experienced serious complications. By the end of the follow-up, 5 of the 8 patients had stopped taking ATDs, 3 of the 8 patients had reduced ATD dosages, and all of them maintained stable thyroid function. Although descriptive analysis was performed because of the small sample size, patients’ thyroid volume reduction, and obvious improvements in goiter symptoms, hyperthyroid symptoms, anxiety, etc., were observed during our follow-up. Conclusion Ultrasound-guided MWA for reducing thyroid gland volume in primary hyperthyroidism patients is a new, safe, and effective minimally invasive treatment method that is a good choice for patients who cannot or refuse traditional treatment methods.

A Systematic Review on Drug-to-Drug Interaction Prediction and Cryptographic Mechanism for Secure Drug Discovery Using AI Techniques

Recently, poly-pharmacy persistence has greatly improved in treating multiple diseases effectively. However, determining potential drug–drug interaction (DDIs) during the drug design is critical for controlling the target clinical drug during secure testing. In the medical field, DDIs are significant for disease diagnosis and treatment, mainly aiding researchers in predicting the link between biomolecules for efficient drug discovery (DD). Artificial intelligence (AI) has recently witnessed researchers accurately predict DDIs at minimum time consumption. Although the AI models show accurate results by aiding physicians to determine poly-pharmacy, several unresolvable issues remain in promoting reliability due to high error, complexity and cost-effectiveness. This paper aims to provide a comprehensive review using AI techniques (machine learning (ML)–deep learning (DL) models) and security enhancement techniques to improve DDI prediction and DD, respectively. The recent state of DDI prediction and the security concerns are presented initially, along with a short discussion about the need for effective techniques. Then, the critical evaluation related to existing studies is analyzed and compared to current issues faced in those existing studies. Various pharmaceutical drugs and their pros are also surveyed in addition to the security analysis for the newly invented drugs. Several assessment measures for the surveyed techniques are also conquered and put forth the need for advancements in future techniques effectively. The performance variations produced by the existing studies are also surveyed, and their use in the medical industry is also provided in this review study. The review of this research encourages the researchers to analyze the various issues faced in the pharmaceutical industry so that a novel technique can be introduced in the upcoming studies.

A Cross-Layer Secure and Energy-Efficient Framework for the Internet of Things: A Comprehensive Survey

This survey delves into cross-layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion detection systems and novel strategies such as blockchain integration. This research aims to promote the development of smart cities by enhancing sustainability, security, and efficiency in the industrial and agricultural sectors through the use of IoT, blockchain, AI, and new communication technologies like 5G. In this paper, we provide a comprehensive review and analysis of secure and energy-efficient cross-layer IoT frameworks based on survey of more than 100 published research articles. We highlight the significance of developing IoT security for robust and sustainable connected systems. We discuss multi-layered security approaches and ways to enhance the energy efficiency of resource-constrained devices in IoT networks. Finally, we identify open research issues and future research directions in the emerging field of cross-layer design for secure and energy-efficient IoT networks. In order to improve cybersecurity and efficiency in smart cities, the research also focuses on developing a secure, energy-efficient IoT framework integrating blockchain, artificial intelligence, and quantum-safe cryptography.

Integrating Blockchain and Homomorphic Encryption to Enhance Security and Privacy in Project Management and Combat Counterfeit Goods in Global Supply Chain Operations

The proliferation of counterfeit goods and data privacy concerns in global supply chain operations poses a significant risk to organizations and end-users alike. This paper explores the integration of blockchain technology and homomorphic encryption as dual mechanisms to enhance security and privacy in project management and supply chain operations. Blockchain offers an immutable, decentralized ledger that provides traceability and authenticity across supply chain nodes, while homomorphic encryption enables secure data processing and analysis without compromising sensitive information. The synergy of these technologies presents a robust framework to address counterfeit risks, facilitating end-to-end product authentication and allowing for secure, real-time collaboration in project management settings. This integration minimizes vulnerabilities in data handling and improves compliance with privacy regulations by securing data at rest and in transit. By evaluating case studies and recent advancements, this paper highlights the potential of blockchain and homomorphic encryption to redefine security protocols, fostering a trustworthy and resilient supply chain ecosystem. Future research directions are recommended to expand the practical applications and address scalability challenges associated with these emerging technologies.

Disaster Risk Reduction Education Through Digital Technologies in the Context of Education for Sustainable Development: A Curricula Analysis of Security and Defense Studies in Serbia

This study examines the integration of disaster risk reduction (DRR) into security and defense studies curricula at Serbian universities, focusing on public and private institutions. As climate change accelerates and natural disasters become more frequent, addressing these risks is critical for national security and sustainable development. This research evaluates the extent of DRR incorporation in curricula and the use of emerging technologies in DRR education. A qualitative analysis of programs at institutions such as the Faculty of Security Studies at the University of Belgrade, the Military Academy, the University of Criminal Investigation and Police Studies, and private universities like Singidunum and Educons University reveals that public institutions have made significant progress. However, private universities still need comprehensive DRR-focused courses and technological integration. This study recommends fostering collaboration between public and private universities, expanding access to the National Simulation Center, and incorporating modern technologies and active learning strategies across curricula to bridge existing gaps. These steps equip future security professionals with the practical skills and interdisciplinary knowledge necessary for effective disaster management in an increasingly complex risk environment.

A comparative analysis of supply chain factors impacting COVID-19 vaccine security in high-income countries (HICs) and low-income and middle-income countries (LMICs)

IntroductionThe COVID-19 pandemic focused attention on the importance of vaccine security to national security. Demand for vaccines far exceeded supply when the first COVID-19 vaccines were released. Growing data suggest...

Privacy-enhanced distributed revocable identity management scheme based self-sovereign identity

In recent years, the rapid proliferation of digital services and resources on Industrial Internet has imposed higher demands on universality and privacy of identity management. Particularly with the advent of the digital economy, prudent users are urged to maintain control over their digital identity credentials. However, traditional identity management methods have failed to meet this requirement and thus have been prone to raise users' concerns about potential financial loss. Specifically, conventional identity management systems(IDMS) have been plagued by imperceptible privacy disclosure, which derives from the flaws in single points of failure, excessive disclosure, correlation analysis, traceability, and revocation. The emerging Self-Sovereign Identity (SSI) architecture aims to tackle these issues and is propelling the evolution of privacy-enhanced distributed identity management. To this end, we proposed a privacy-enhanced distributed identity management scheme with sequential aggregate issuance, threshold traceability and revocability in the setting of multiple credential issuers and regulators. We adopted the Decentralized identifiers(DIDs) and verifiable credentials(VCs) based on the SSI architecture to ensure the hierarchical identity authentication. The security and performance analysis shows that our proposal achieves the desired design goals and is feasible for distributed Industrial Internet.

An Efficient EHR Secure Exchange Among Healthcare Servers Using Light Weight Scheme

This work addresses the critical need for secure and patient-controlled Electronic Health Records (EHR) migration among healthcare hospitals' cloud servers (HHS). The relevant approaches often lack robust access control and leave data vulnerable during transfer. Our proposed scheme empowers patients to delegate EHR migration to a trusted Third-Party Hospital (TTPH); which is the Certification Authority (CA) while enforcing access control. The system leverages asymmetric encryption utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA), EEC and ECDSA added robust security and lightness EHR sharing. Patient and user privacy is managed due to anonymity through cryptographic hashing for data protection and utilizes mutual authentication for secure communication. Formal security analysis using the Scyther tool and informal analysis was conducted to validate the system's robustness. The proposed scheme achieved EHR integrity due to the verification of the communicated HHS and ensuring the integrity of the HHS digital certificate during EHR migration. Ultimately, the result achieved in the proposed work demonstrated the scheme's high balance between data security and accuracy of communication, where the best result obtained represented 7.7/ ms as computational cost and 1248 /bits as communication cost compared with the relevant approaches.

RELAKA: Robust ECC based Privacy Preserving Lightweight Authenticated Key Agreement protocol for healthcare applications

With the advancement of cutting-edge technologies, the Internet of Medical Things (IoMT) has assisted the healthcare sector by facilitating interaction between healthcare service providers and patients in remote areas. In IoMT, wearable or implantable sensors collect the patient’s record and share the information through a public network. Health-related information about the patient must be protected from a variety of attacks by the adversary since it is sensitive and extremely vulnerable to attacks. The sensor equipment that is implanted in the patient is also resource-constrained and has a low power capacity. The entities involved in the communication must be authenticated with one another in order to protect patients’ health information, anonymity, and reliability. While several authenticated key agreement protocols have been proposed, many suffer from high computational costs and storage cost, making them unsuitable for lightweight applications. This paper proposes a secure three-factor robust Elliptic Curve Cryptography (ECC) based mutually authenticated and key agreement protocol known as RELAKA for the IoMT environment, utilizing the benefits of one-way hash function. In proposed scheme, all entities, including the healthcare service providers and wearable sensors, are authenticated by the medical server. Subsequently, a secret key is established for each communication session and shared between all the entities. Additionally, mechanism for appropriate user revocation and re-registration is integrated to provide additional security in cases where a user’s QR code is tampered with by the attacker. The privacy of the proposed protocol is investigated by the potential use of zero knowledge proof. Furthermore, the efficacy of the authentication is examined by challenge and response mechanism. The informal security analysis demonstrates its resistance to threats such as DoS, impersonation, message modification, password guessing, and so on. The performance evaluation of RELAKA protocol indicates that the execution, communication, and storage costs is reduced by 87.59%, 43% and 60.71% respectively. Moreover, the outcomes of the AVISPA simulation illustrate that the RELAKA successfully evades both active and passive attacks. In addition, real-world testbed environment is developed with Raspberry pi 4 model B and the experimental results verifies the robustness of the proposed protocol. According to theoretical analysis and experimental evaluation, the RELAKA scheme is more secure and efficient than the existing protocols.

A New Tree Quantum Key Agreement Protocol for Secure Multiparty Communication

The Tree Multiparty Quantum Key Agreement Protocol (TMQKAP) is introduced as a novel solution for secure quantum key agreement among multiple participants, specifically tailored for tree topologies. Based on the BB84 protocol, TMQKAP employs hierarchical tree structures and XOR operations to facilitate efficient and secure key generation. Key elements are exchanged among participants in an equitable manner, ensuring that each participant contributes equally to the generation of the shared key. The protocol demonstrates robust security, effectively defending against both external and internal attacks, and achieves a quantum efficiency of 1/2 (N −1), where N is the number of participants. Thorough security analysis and simulations show TMQKAP’s robustness against various attacks while maintaining high efficiency. Additionally, the protocol is readily implementable with current quantum technologies, utilizing single-photon transmission to facilitate secure key distribution.