Security Protocols Research Articles

Designing secure data pipelines for medical billing fraud detection using homomorphic encryption and federated learning

Medical billing fraud imposes significant financial and operational challenges on healthcare systems, highlighting the need for robust, privacy-preserving fraud detection solutions. This study presents a secure data pipeline that integrates homomorphic encryption (HE) and federated learning (FL) to enable decentralized fraud detection while maintaining patient confidentiality. Homomorphic encryption ensures data remains protected throughout the analytical process, while federated learning facilitates collaborative model training across healthcare institutions without requiring data centralization. Key findings reveal that increasing privacy levels via differential privacy effectively reduces data leakage risks, though it introduces minor computational overhead and a slight reduction in model accuracy. Scalability tests show that larger datasets considerably increase encryption time and memory usage, underscoring the need for optimized encryption algorithms. Additionally, secure communication protocols, while essential for data integrity, result in increased latency, which may impact real-time detection capabilities. The proposed pipeline achieves a balance between security and fraud detection accuracy, demonstrating its potential for real-world applications. However, further optimization of encryption methods and secure communication protocols is essential for broader scalability. This work advances privacy-centric approaches in healthcare fraud detection, setting a foundation for developing secure, scalable fraud detection systems.

API C4E Augmentation: AI-Powered Agent(AIPA) Framework

Abstract: In today’s rapidly evolving digital landscape, organizations increasingly rely on Application Programming Interface known as API for seamless integration and data exchange. API providers comprising consumers, developers, and project teams face several challenges in discovering, developing and mapping APIs in compliance with organizational enterprise architecture principles. This paper aims to address these challenges by proposing an AI-Powered Agent (AIPA) Framework that leverages Generative AI and AI Code Assistant tools to enhance API governance and streamline the API development cycle (API Management, develop API, testing, security, deployment and monitoring). The proposed framework facilitates API discovery through an interactive chat interface that summarizes available APIs based on the use case. Additionally, it aims to automate key aspects of API development, including compliance checks, security protocols, and document generation thereby significantly reducing effort(s), human error(s), and improving productivity and efficiency. Establishing an API Center for Enablement (C4E) ensures consistent adoption of best practices across the organization. By integrating AI-driven solution with API governance, this paper outlines a pathway for organizations to improve API quality, security, and usability while empowering to adapt to digital disruptions. The findings suggest that a comprehensive, AI-enhanced governance framework enhances operational and development efficiency and fosters innovation.

FS2M: fuzzy smart IoT device pairing protocol via speak to microphone

Purpose This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context. Design/methodology/approach This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography. Findings This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency. Originality/value In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

Implementing Enterprise-Scale iOS MDM : A Technical Deep Dive

As organizations increasingly adopt mobile technologies, the need for effective remote management of iOS devices has grown significantly. This technical article examines implementation strategies and real-world performance metrics from a study of Mobile Device Management (MDM) implementations across 12 diverse organizations, encompassing 2,500 enterprise devices during 2023-2024. The article reveals that optimized MDM solutions can achieve 99.99% uptime while reducing infrastructure costs by up to 45%. Key findings demonstrate that effective MDM implementation reduces device management costs by 64%, improves security compliance rates to 99.3%, and decreases incident response time by 73%. Through automated troubleshooting and optimization, organizations experienced a 67% reduction in security incidents and a 42% decrease in IT support overhead while achieving 85% reduction in deployment time and 82% acceleration in app deployment processes. The article comprehensively evaluates architecture design, deployment metrics, security implementation, performance analysis, and scalability considerations. Implementation of advanced security protocols resulted in a 98.7% prevention rate for common mobile security threats, while maintaining 99.99% system reliability. The article also highlights the importance of phased deployment strategies, which demonstrate 43% higher success rates compared to direct full-scale implementations, and provides strategic insights for future planning and infrastructure optimization.

Integrating Artificial Intelligence in Autonomous Cashier Systems: A Study on Functional Schema Design and Its Impact on Supermarket Operations

This integrative literature review (ILR) explores the transformative potential of artificial intelligence (AI) in optimizing in-store logistics, a crucial aspect for retail environments striving to meet growing consumer demands for convenience and quick product availability. It aims to provide retail managers and logistics specialists with actionable insights on AI-driven tools that enhance competitiveness by improving inventory management and space utilization in retail settings. The conceptual framework is framed by functional schema design, user acceptance and experience, operational efficiency, data security and privacy, and equitable access, emphasizing the ethical and inclusive implementation of technology. Key findings suggest that AI improves demand accuracy, reduces inventory mismatches, and enables adaptable store layouts that respond dynamically to real-time consumer behavior, creating a more efficient, responsive, and customer-centered retail environment. Additionally, this study examines the adoption of AI in cashier systems as a response to evolving consumer expectations for efficient digital shopping experiences, revealing that AI-enabled cashier systems significantly reduce wait times and operational costs while providing personalized experiences through real-time analytics. However, challenges such as robust data security protocols, inclusive design for diverse user groups, and employee reskilling remain critical as automation progresses. Recommendations focus on privacy-enhancing data practices, user-friendly interfaces, and hybrid solutions combining human support with automation to foster inclusivity and consumer trust. Future research should address the long-term impacts of AI in retail, cultural adaptability, customer engagement, and secure data handling to support a balanced, customer-centered approach to automation in the retail sector.

Seamless Transition to Post-Quantum TLS 1.3: A Hybrid Approach Using Identity-Based Encryption

We propose a novel solution to streamline the migration of existing Transport Layer Security (TLS) protocol implementations to a post-quantum Key Encapsulation Mechanism for Transport Layer Security (KEMTLS). By leveraging Identity-Based Encryption (IBE), our solution minimizes the necessary modifications to the surrounding infrastructure, enabling the reuse of existing keys and certificates. We provide a proof-of-concept implementation and performance analysis, demonstrating the practical feasibility and effectiveness of our proposed approach.

Assessing Satellite-Augmented Connected Vehicle Technology for Security Credentials and Traveler Information Delivery

Vehicle-to-Everything (V2X) technology has the capability to enhance road safety by enabling wireless exchange of telematics and spatiotemporal information between connected vehicles (CVs). Effective V2X communication depends on rapid information sharing between Roadside Units (RSUs), in-vehicle On-Board Units (OBUs), and other connected infrastructure. However, there are increasing concerns with RSUs related to installation needs, reliability, and coverage, especially on rural roadways. This study aims to evaluate the benefits of augmenting CV infrastructure with satellite technology in situations where RSU access or coverage is limited while maintaining V2X security protocols and critical information exchange. The study utilizes data from over 400 personal, fleet, and commercial CVs collected during two real-world pilot deployments in the United States, one in an urban environment in Florida and one in a rural environment in Wyoming. The analysis performed shows that the delivery of critical security credential information and traveler information messages (TIMs) to CVs is dependent on a multitude of environmental and operational reliability factors. Overall, information delivery is faster with dense RSU infrastructure as compared to satellites. However, we show that by augmenting RSU infrastructure with satellite technology, the delivery of information is more robust, improving V2X system reliability, security, and overall road safety.

Implementing federated learning over VPN-based wireless backhaul networks for healthcare systems

Federated learning (FL) is a popular method where edge devices work together to train machine learning models. This study introduces an efficient network for analyzing healthcare records. It uses VPN technology and applies a federated learning approach over a wireless backhaul network. The study compares different wireless backhaul channels, including terahertz (THz), E/V band (mmWave), and microwave, for their effectiveness. We looked closely at a suggested FL network that uses VPN technology over awireless backhaul network. We compared it with the standard method and found that using the FedAvg algorithm with Terahertz (THz) for communication gave the best accuracy. The time it took to reach a conclusion improved a lot, going from 55 seconds to an impressive 38 seconds. This emphasizes how having a faster communication link makes FL networks work much better. Furthermore, a three-step plan was executed to boost security, adopting a multi-layered method to safeguard the FL network and its confidential data. The first step involves integrating a private network into the current telecom infrastructure, establishing an initial layer of security. To enhance security further, licensed frequency channels are introduced, providing an extra layer of protection. The highest level of security is achieved by combining a private network with licensed frequency channels, complemented by an additional layer of security through VPN-based measures. This comprehensive strategy ensures the application of strong security protocols.

Exploration of Reliability and Security of E- Banking: Selected Urban Cooperative banks

The rise of E-banking has introduced unprecedented convenience and accessibility for users, transforming traditional banking operations into efficient online experiences. However, this shift brings challenges, particularly in terms of security and reliability, as users now expect robust protections for their financial data and consistent, dependable service. This study investigates the perceptions of security and reliability in digital banking among 290 urban cooperative banking customers. Using factor analysis, we explore key dimensions of e-banking services, focusing on elements like encryption, biometric security, and trust in transaction reliability. The analysis identified two primary factors: security and reliability, each with distinct contributions to overall user satisfaction. The factor loadings revealed that users had mixed confidence in encryption technologies and data privacy measures, with some expressing high levels of satisfaction, while others indicated significant concerns. Additionally, reliability emerged as a critical factor, with findings indicating that a notable portion of customers were dissatisfied with the timeliness and trustworthiness of e-banking services. For data collection, a structured survey measured responses on a five-point Likert scale, assessing variables such as trustworthiness, encryption effectiveness, privacy maintenance, and personal security practices. Through the use of descriptive and factor analysis, we quantified customer sentiments and identified patterns in user satisfaction. The results suggest that while there is moderate confidence in digital banking security, the reliability of service delivery remains an area for improvement. Banks should consider addressing these findings by enhancing transparency in their security protocols and strengthening service consistency to better meet user expectations in digital banking environments.

NIGERIA’S SECURITY DYNAMICS AND THE FIGHT AGAINST CRIME

This review paper delves into the 'security dynamics in Nigeria, and the fight against crime', elucidating the current state of criminal activities in Nigeria, which have reached unprecedented levels, particularly with the emergence of social media and other sophisticated technological tools. Nevertheless, the paper aims to leverage these advanced technological resources for intelligence gathering to combat crime effectively. It correlates the nature of criminal activities and their evolution with the intergenerational differences present in our society today, highlighting how these differences have also impacted the capabilities of law enforcement agencies in addressing crime in Nigeria. The recommendations put forth in this paper emphasize the significance of inter-agency cooperation, advocating for the integration of advanced intelligence-gathering mechanisms into the operational frameworks of security agencies to expedite crime prevention. Additionally, the discussion on recommendations entails investing in community policing, enhancing security protocols, and enacting reforms, among other proposals.

On the quantum security of high-dimensional RSA protocol

Abstract The idea of extending the classical RSA protocol using algebraic number fields was introduced by Takagi and Naito (Construction of RSA cryptosystem over the algebraic field using ideal theory and investigation of its security. Electron Commun Japan Part III Fund Electr Sci. 2000;83:19–29). Recently, Zheng et al. proposed the use of the ring of algebraic integers of an algebraic number field and the lattice theory to present a high-dimensional form of RSA. The authors claim that their proposal is post-quantum and is significant both from the theoretical and practical point of view. In this article, we prove that the security of Zheng et al.’s scheme is still based on the factorization problem, and we present a practical quantum attack on this proposed scheme, our attack is a quantum polynomial time algorithm that employs Shor’s algorithm as a subroutine.

Innovative Cloud Architectures: Revolutionizing Enterprise Operations Through AI Integration

This article examines the transformative impact of artificial intelligence (AI) and machine learning (ML) integration in cloud computing architectures across enterprise operations. Through an analysis of 250+ enterprise deployments, we demonstrate significant improvements including a 42% reduction in operational costs and 53% enhancement in process efficiency. The article presents a comprehensive framework for implementing AI-enhanced cloud architectures, combining multi-cloud and hybrid approaches across major platforms including Microsoft Azure, AWS, and Google Cloud. Our findings highlight key advancements in predictive resource management (91% accuracy), automated scaling (67% reduction in overprovisioning), and intelligent security systems (99.7% threat detection accuracy). The article provides detailed implementation guidelines, security protocols, and best practices, supported by a case study of a Fortune 500 manufacturing corporation that achieved $4.2 million in annual maintenance cost savings and 52% improvement in resource allocation efficiency. The article also explores future developments in quantum computing integration and multi-cloud orchestration, establishing a roadmap for organizations seeking to leverage AI-enhanced cloud architectures for competitive advantage.

Timed Interpreted Systems as a New Agent-Based Formalism for Verification of Timed Security Protocols

This article introduces a new method for modelling and verifying the execution of timed security protocols (TSPs) and their time-dependent security properties. The method, which is novel and reliable, uses an extension of interpreted systems, accessible semantics in multi-agent systems, and timed interpreted systems (TISs) with dense time semantics to model TSP executions. We enhance the models of TSPs by incorporating delays and varying lifetimes to capture real-life aspects of protocol executions. To illustrate the method, we model a timed version of the Needham–Schroeder Public Key Authentication Protocol. We have also developed a new bounded model checking reachability algorithm for the proposed structures, based on Satisfiability Modulo Theories (SMTs), and implemented it within the tool. The method comprises a new procedure for modelling TSP executions, translating TSPs into TISs, and translating TISs’ reachability problem into the SMT problem. The paper also includes thorough experimental results for nine protocols modelled by TISs and discusses the findings in detail.

Data-Driven Revenue Cycle Management: Optimizing claims denial resolution and under payment for healthcare providers through SaaS Solution

The healthcare industry is increasingly under financial pressure due to high rates of claims denials, leading to substantial revenue loss and operational inefficiencies. Claims denials result from various factors, including coding errors, incomplete documentation, and regulatory non-compliance, all of which contribute to revenue leakage that often reaches 5–10% annually for healthcare providers. This paper explores a proprietary Software-as-a-Service (SaaS) solution that leverages data analytics, machine learning, and real-time reporting to enhance the claims denial management process. Through predictive and prescriptive analytics, the proposed solution enables healthcare providers to identify denial trends, reduce denial rates, optimize claims resolution, and increase revenue retention. The paper includes system architecture, data security protocols, pilot implementation findings, and a comparative analysis of performance metrics. The paper concludes with a discussion on future developments, including real- time denial prediction, automated appeals generation, and enhanced integration capabilities with existing Electronic Health Record (EHR) systems. Keywords: Revenue Cycle Management, healthcare claims denial, claim under payment, SaaS, data analytics, machine learning, real-time reporting, financial sustainability, healthcare providers

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.

From Grapes to Clicks : A Cloud-Based Wine-Selling Solution for Single Vendors

This document outlines the design and creation of a robust wine vending application for a single vendor, which combines a web platform, an Android mobile app, and a cloud-based framework. This solution addresses the increasing demand for online wine sales while delivering a smooth, secure, and adaptable shopping experience. Essential features of the system include an intuitive web interface and an application focused on effective product searching, ordering, and payment processing. Cloud infrastructure is implemented to guarantee high availability, scalability, and secure data management. The paper discusses various architectural alternatives, such as utilizing cloud services for load balancing, data handling, and disaster recovery. It also emphasizes the necessity of strong security protocols, including encryption, user authentication, and adherence to industry standards for online transactions. Additionally, the document addresses challenges related to regulatory compliance, including age verification mandates and data protection laws. Lastly, it explores strategies for performance enhancement, improvements to user experience, and potential avenues for future expansion. The proposed solution aims to deliver a dependable, secure, and scalable platform tailored to the requirements of individual wine retailers while accommodating future growth.

Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system’s effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristic of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount.

Quantum intrusion detection system using outlier analysis

In the field of cybersecurity, hackers often enter computer systems despite current security measures, owing to the huge amount of network traffic that makes intruder identification difficult. Differentiating between authorized traffic and abnormal data produced by Distributed Denial of Service (DDoS) attackers is still a major difficulty. This research provides a unique technique that uses Quantum Machine Learning (QML) to improve security protocols for secure communication between two parties. Our strategy enhances detection accuracy and speed by using the characteristics of quantum neural networks. The approach includes creating a dataset from network traffic patterns, preprocessing it, and then turning it into quantum bits via angle embedding. The research uses outlier analysis, min-entropy, and quantum state fidelity to distinguish between normal and abnormal data patterns. The dispersed randomness of network header data is measured using entropy, which aids in identifying security concerns. The suggested QML-based methodology outperforms conventional approaches and current models, including AMM-CNN and ANN models, with a detection accuracy of 99.87% for DDoS attacks. This advancement leads to more effective and secure communication networks.

Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations

This study proposes a novel forgery detection method based on the analysis of frequency components of images using the Discrete Fourier Transform (DFT). In recent years, face manipulation technologies, particularly Generative Adversarial Networks (GANs), have advanced to such an extent that their misuse, such as creating deepfakes indistinguishable to human observers, has become a significant societal concern. We reviewed two GAN architectures, StyleGAN and StyleGAN2, generating synthetic faces that were compared with real faces from the FFHQ and CelebA-HQ datasets. The key results demonstrate classification accuracies above 99%, with F1 scores of 99.94% for Support Vector Machines and 97.21% for Random Forest classifiers. These findings underline the fact that performing frequency analysis presents a superior approach to deepfake detection compared to traditional spatial detection methods. It provides insight into subtle manipulation cues in digital images and offers a scalable way to enhance security protocols amid rising digital impersonation threats.

Secure and Lightweight Cluster-Based User Authentication Protocol for IoMT Deployment

Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in more and more patients’ privacy being threatened. Different from traditional IoT devices, sensors are generally considered to be based on low-cost hardware designs with limited storage resources; thus, authentication techniques for IoMT scenarios might not be applicable anymore. In this paper, we propose an efficient three-factor cluster-based user authentication protocol (3ECAP). Specifically, we establish the security association between the user and the sensor cluster through fine-grained access control based on Merkle, which perfectly achieves the segmentation of permission. We then demonstrate that 3ECAP can address the privilege escalation attack caused by permission segmentation. Moreover, we further analyze the security performance and communication cost using formal and non-formal security analysis, Proverif, and NS3. Simulation results demonstrated the robustness of 3ECAP against various cyber-attacks and its applicability in an IoMT environment with limited storage resources.