Authentication Mechanism Research Articles

RAM-MEN: Robust authentication mechanism for IoT-enabled edge networks

The rapid expansion of Mobile Edge Computing (MEC) and the Internet of Things (IoT) has revolutionized technology by enabling real-time data processing at the network edge, which is essential for applications such as autonomous vehicles and smart cities. With the advent of 6G networks, which promise ultra-fast speeds, vast connectivity, and low-latency communication, MEC-IoT systems are becoming more powerful but also face significant security challenges. Existing authentication mechanisms (AMs) are often vulnerable to attacks like impersonation and insider threats. This paper introduces a novel lightweight AM, called RAM-MEN that employs cryptography and physically unclonable functions (PUFs) to secure IoT-enabled MEC environments in the 6G era. It protects against insider threats and fake MEC access points while ensuring efficiency and scalability. Additionally, the proposed RAM-MEN establishes a secure communication channel (session key) between IoT devices and the MEC server, enabling secure offloading of computationally intensive tasks. The security of the session is rigorously evaluated using formal methods, including Scyther and the random or real model, alongside informal approaches. Comparative performance evaluations show that the proposed RAM-MEN reduces communication costs by 21.54% to 45.53% and computational costs by 17.09% to 83.72%, while providing enhanced security features.

A Novel Authentication Mechanism with Efficient Mathematic Based Technique

A Novel Authentication Mechanism with Efficient Mathematic Based Technique

Reverse Engineering GitHub CoPilot: Creating an OpenAI-Compatible Endpoint for Enhanced Developer Integration

This paper presents the reverse engineering of GitHub CoPilot to develop an OpenAI-compatible endpoint, enabling broader access and integration possibilities for AI-assisted code completion. By analyzing CoPilot's communication protocols and creating a proxy server that translates OpenAI API requests to CoPilot's internal API, we bridge the gap between proprietary tools and open standards. The implementation, allows developers to utilize CoPilot's capabilities within their preferred environments using the familiar OpenAI API interface. We detail the system architecture, authentication mechanisms, request processing pipeline, and performance optimization techniques. Our results demonstrate successful integration, with robust performance metrics, including low response times and high compatibility rates. This work opens avenues for enhanced developer productivity and flexibility in AI-assisted coding tools.

Design and Optimization of Reconfigurable Intelligent Surfaces Structures

This research investigates the design and optimization of reconfigurable intelligent surfaces (RIS) structures to enhance simultaneous wireless information and power transfer (SWIPT) in cooperative relay networks. The integration of RIS technology with SWIPT offers promising opportunities to improve the energy efficiency and spectral efficiency of wireless communication systems. In this study, we propose novel RIS deployment strategies and optimization algorithms to maximize the performance of SWIPT-enabled cooperative relay networks. First, we analyze the fundamental principles of RIS-assisted SWIPT and cooperative relay networks, considering the unique characteristics and challenges of each component. We then formulate optimization problems to jointly optimize the placement of RIS elements, relay selection, transmit power allocation, and RIS phase configuration to achieve the desired trade-off between communication reliability, energy efficiency, and spectral efficiency. Furthermore, we develop efficient algorithms for channel estimation, feedback, and coordination among RIS, relays, and destination nodes to adapt to dynamic channel conditions and user requirements. We also investigate the impact of RIS deployment on the energy harvesting performance of SWIPT-enabled relay nodes, considering factors such as RIS reflection coefficients, relay-RIS distance, and incident signal power distribution. Moreover, we address security and privacy considerations in RIS-assisted SWIPT, including potential vulnerabilities, authentication mechanisms, and privacy-preserving communication techniques. Finally, we validate the proposed design and optimization techniques through comprehensive simulations and real-world experiments, considering practical constraints such as hardware implementation, energy harvesting efficiency, and deployment scenarios. Overall, this research contributes to advancing the understanding and practical implementation of RIS technology for SWIPT in cooperative relay networks, offering insights into the design, optimization, and performance evaluation of next-generation wireless communication systems.

How to Improve the Security of Password Checkers

After years of the attempt to replace password with other alternatives such as biometrics and smart cards, password is still the most pervasive user authentication mechanism. The password checking authentication is widely used for financial services, online social networks, and many other applications. This paper aims to analyze the security of a <i>password checker</i> qualitatively and quantitatively, and show how to improve it. <i>Qualitative</i> security analysis, in which it does not allow any information flow from secret date to public data, considers that the password checker is not a secure process. Therefore, an alternative analysis for the password checker is to analyze <i>quantitatively,</i> i.e., quantifying its information flow and determining how much secret information has been leaked. This method can be used to decide whether we can tolerate small leakages. A quantitative security analysis can be seen as a generalization of a qualitative one. To improve the security of the password checker, we propose a <i>noisy-output</i> policy, i.e., a situation where a system operator is able to add noise to the output: instead of always producing the exact outcomes, the system sometimes reports noisy outcomes. The noisy outcomes reduce the correlation between the output and the input, and thus reduce the leakage.

IMPLEMENTATION OF DIGITAL TWINS BASED ON GPT-3.5 FOR ENHANCING ENGLISH LANGUAGE LEARNING IN HIGHER EDUCATION INSTITUTIONS

This paper presents the development of an interactive web-based platform aimed at enhancing English language learning through the utilization of digital twins, powered by the GPT-3.5 model. The platform integrates three primary services essential for language learning: vocabulary acquisition, grammar checking, and conversational practice via simulated interlocutors, offering a dynamic, personalized learning experience. Detailed technical aspects of the system’s architecture are discussed, highlighting the implementation of the Model-View-Controller (MVC) framework, which ensures modularity and scalability. The platform’s backend is developed using C# for reliability and performance, while the frontend leverages HTML, CSS, JavaScript, and the Bootstrap framework to create a responsive, user-friendly interface that adapts to various screen sizes and devices. To support large volumes of user data, a Microsoft Azure SQL Database is employed for robust data management, enabling efficient storage and retrieval of user information, interaction histories, and progress logs. Integration with GPT-3.5 via OpenAI's API facilitates real-time query processing and response generation, making the platform a powerful tool. The platform uses advanced personalization algorithms to adjust learning content based on user preferences, progress, and interaction history. This personalized approach increases student engagement and promotes more effective learning outcomes by adapting content to individual needs. Security and ethical considerations are addressed through encryption protocols and authentication mechanisms. In addition, OpenAI’s built-in content filtering systems ensure that inappropriate or harmful content is blocked, safeguarding users while maintaining high ethical standards. Furthermore, a detailed cost calculation model for token usage is presented, which allows for precise tracking of operational costs, ensuring the platform’s long-term sustainability. By integrating cutting-edge technology with pedagogical principles, this platform demonstrates the potential to revolutionize English language learning in higher education institutions, making it more interactive, personalized, and effective.

User Authentication System Using Python

User authentication is a critical component in ensuring the security and privacy of digital systems. This paper User authentication is a critical component in ensuring the security and privacy of digital systems. This paper explores the implementation of user authentication explores the implementation of user authentication systems using Python, a versatile and widely-used systems using Python, a versatile and widely used programming language. With its robust libraries and frame works, Python provides a comprehensive eco system for designing, developing, and deploying secure authentication mechanisms. The study focuses on the practical implementation of authentication methods, including password-based systems, two-factor authentication (2FA), and biometrics integration. Python libraries such as Flask and Django are employed to create backend systems, while libraries liked crypt and pass lib are utilized for hashing and securing passwords. The integration of 2FA is demonstrated using PyOTP, which facilitates one-time password generation to enhance security. This paper also highlights the role of JSON Web Tokens (JWT) in enabling secure, stateless user sessions, making them suitable for modern web and mobile applications. Python's inter operability with biometric authentication systems is explored through the use of APIs and machine learning libraries, providing a foundation for advanced authentication mechanisms. The proposed authentication system is evaluated for its security, scalability, and ease of implementation, offering insights into best practices for minimizing vulnerabilities such as brute force attacks and data breaches. Additionally, the paper emphasizes the importance of encrypting sensitive user data during transmission using libraries like cryptography and SSL/TLS protocols. In conclusion, the paper demonstrates Python's potential to implement secure, flexible, and scalable user authentication systems suitable for diverse applications. It serves as a guide for developers and researchers aiming to enhance the security of their systems through robust authentication techniques. Programming language with its robust libraries and frameworks, Python provides a comprehensive eco system for designing, developing, and deploying secure authentication mechanisms. The study focuses on the practical implementation of authentication methods, including password-based systems, two-factor authentication (2FA), and biometrics integration. Python libraries such as Flask and Django are employed to create backend systems, while libraries like bcrypt and passlib are utilized for hashing and securing passwords. The integration of 2FA is demonstrated using PyOTP, which facilitates one-time password generation to enhance security.

Building Patient Trust through Enhanced Data Security: A Saudi Arabian Hospital Case Study

Purpose: The study highlights the need for healthcare providers to prioritize the implementation of enhanced authentication mechanisms and consent management systems to align with patient expectations and build trust. Additionally, the research emphasizes the importance of addressing the knowledge gap among healthcare staff through mandatory training programs and clear communication strategies. The proposed action plan outlines concrete steps to enhance data security and transparency, positioning the hospital as a leader in safeguarding patient information. Methodology: A survey was conducted among patients in a private hospital setting to assess their perceptions and expectations regarding the security of their medical information. The survey explored patient awareness of data security risks, preferences for authentication methods, and views on consent protocols for accessing health records. Originality and implication: This study provides valuable insights into patient perspectives on data security within a private hospital setting, an area often overlooked in broader healthcare data security research. The findings have significant implications for healthcare providers, emphasizing the need to align security practices with evolving patient expectations to maintain trust and ensure the responsible handling of sensitive medical information. Keywords: Patient Data Security, Healthcare Data Privacy, Patient Consent, Authentication Methods, OTP Verification, Electronic Health Records, Security Awareness Training, Trust in Healthcare, Private Hospital Setting

Cyber security of unmanned aviation complexes and features of protection against interception.

The article considers the key aspects of cyber security of unmanned aerial systems (UAS) in the conditions of modern military conflicts and technological progress, unmanned systems have become important tools for gathering information, conducting intelligence and performing combat missions. However, their widespread use is accompanied by new cyber threats that require specialized measures to ensure security. The article analyzes the main types of cyberthreats that can affect UAS, including the interception of communications, unauthorized access to management systems and the breaking of cryptographic protections. The article also discusses modern protection methods and technologies, such as cryptographic algorithms, authentication mechanisms and data encryption. In addition, existing standards and practical solutions to ensure the protection of communication channels are analyzed. The last part of the article is devoted to the latest trends in the field of cyber security of UAS and forecasting future threats. Possible solutions and approaches for improving the protection of unmanned systems in the rapidly changing cyber environment are considered, as well as recommendations are provided for improving the security systems of UAS, which include the integration of new technologies and adaptation to new threats

Cyber Attacks in Extended Reality (XR) Using AI: Malicious Programs and Mitigation Strategies

The convergence of artificial intelligence (AI) and extended reality (XR) technologies, such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), has introduced new dimensions of immersive experiences. However, the combination of AI and XR has also created fertile ground for cybercriminals to exploit these advanced platforms. This paper explores the types of AI-driven malicious programs targeting XR systems, including AI-enhanced phishing, malware, deepfake avatars, ransomware, and behavioral manipulation. Additionally, it outlines strategies to mitigate these AI-driven cyberattacks, such as advanced threat detection, secure authentication mechanisms, regular security audits, user education, and AI-enhanced security solutions. As XR becomes increasingly integrated into daily life, understanding and addressing AI-driven cyber threats is crucial to ensure secure virtual environments.

Few-Shot Metric Learning with Time-Frequency Fusion for Specific Emitter Identification

Specific emitter identification (SEI) is a promising physical-layer authentication technique that serves as a crucial complement to upper-layer authentication mechanisms. SEI capitalizes on the inherent radio frequency fingerprints stemming from circuit discrepancies, which are intrinsic hardware properties and challenging to counterfeit. Recently, various deep learning (DL)-based SEI methods have been proposed, achieving outstanding performance. However, collecting and annotating substantial data for novel or unknown radiation sources is not only time-consuming but also cost-intensive. To address this issue, this paper proposes a few-shot (FS) metric learning-based time-frequency fusion network. To enhance the discriminative capability for radiation source signals, the model employs a convolutional block attention module (CBAM) and feature transformation to effectively fuse the raw signal’s time domain and time-frequency domain representations. Furthermore, to improve the extraction of discriminative features under FS scenarios, the proxy-anchor loss and center loss are introduced to reinforce intra-class compactness and inter-class separability. Experiments on the ADS-B and Wi-Fi datasets demonstrate that the proposed TFAF-Net consistently outperforms existing models in FS-SEI tasks. On the ADS-B dataset, TFAF-Net achieves a 9.59% higher accuracy in 30-way 1-shot classification compared to the second-best model, and reaches an accuracy of 85.02% in 10-way classification. On the Wi-Fi dataset, TFAF-Net attains 90.39% accuracy in 5-way 1-shot classification, outperforming the next best model by 6.28%, and shows a 13.18% improvement in 6-way classification.

GDAR: A Secure Authentication for Dapp Interoperability in Blockchain

Enterprises are adopting blockchain technology to build a server-less and trust-less system by assuring immutability and are contributing to blockchain research, innovation, and implementation. This led to the genesis of various decentralized blockchain platforms and applications that are unconnected with each other. Interoperability between these siloed blockchains is a must to reach its full potential. To facilitate mass adoption, technology should have the ability to transact between various decentralized applications (dapps) on the same chain, integrate with existing systems, and initiate transactions on other networks. In our research, we propose a secured authentication mechanism that enables various decentralized applications on the same chain to interact with each other using a global dapp authentication registry (GDAR). We carried out an in-depth performance evaluation and conclude that our proposed mechanism is an operative authentication solution for dapp interoperability.

ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks

When it comes to the development of 4G and 5G technologies, long-range IoT or machine-to-machine (M2M) communication can be achieved with the help of cellular infrastructure. In non-standalone (NSA) 5G infrastructure, cellular-IoT (C-IoT) devices are attached and authenticated by a 4G core network even if it is connected to a 5G base station. In an NSA-based 5G network, the presence of dual connectivity sometimes raises interoperability and authentication issues due to technological differences between LTE and 5G. An attacker explores these technological differences, introduces the threats, and performs various types of attacks like session hijacking at the interfaces and Man-in-the-Middle (MITM) attacks. With the introduction of these attacks, the attackers exploit the network resources and pinch out various critical information sources. To resolve this issue, the NSA-based C-IoT network must incorporate robust and seamless authentication and authorization mechanisms. This article presents the ML-AKA protocol that is used to enhance interoperability and trust between 4G and 5G networks by using a uniform key-sharing (UKS) mechanism. The proposed ML-AKA protocol is analyzed with the help of the AVISPA tool and validated with the use of Proverif. Further, the proposed protocol is compared with other existing protocols like EPS-AKA and UAKA-D2D, and the outcome shows that the proposed protocol significantly reduces the chances of MITM, DDOS and Spoofing attacks during the interoperability in the NSA-C-IoT network.

Hybrid Mutual Authentication for Vehicle-to-Infrastructure Communication Without the Coverage of Roadside Units

The security issues in Vehicle Ad Hoc Networks (VANETs) are prevalent within Intelligent Transportation Systems (ITS). To ensure the security of vehicle-to-infrastructure (V2I) communication, extensive research on V2I authentication has been conducted in recent years. However, these protocols often overlook the limitations of communication range, leading to failures in V2I communication. Consequently, addressing the challenge of secure V2I communication in areas not covered by distributed roadside units (RSUs) remains a significant task. To address these issues, the current study proposes an Anonymous Certificate-less Hybrid Mutual Authentication Protocol (ACHMAP) based on Vehicle-to-Vehicle-to-Infrastructure (V2V2I) communication. In the proposed protocol, a secure multi-hop link is established through vehicle-to-vehicle (V2V) mutual one-time token authentication. Subsequently, the out-of-coverage vehicle and relevant RSUs complete V2I mutual authentication using signcryption messages transmitted by vehicle nodes. In the security analysis, it is demonstrated that the entire V2V2I stage can resist various security attacks, such as replay attacks, impersonation attacks, and threats to user anonymity, while preserving confidentiality and integrity. We simulated the proposed protocol using Network Simulator 3 (NS-3) to confirm that the authentication mechanism has lower overhead and minimal authentication delay in V2V2I communication.

IZKP-AKA: A secure and improved ZKP-AKA protocol for sustainable healthcare

The use of IoT in healthcare has undoubtedly brought many significant adaptations and benefits that changed medical facilities. However, the possibility of unauthorized access to private medical data is a serious issue that requires appropriate attention to protect the user’s privacy. Recently, a proposed scheme by Gurjot et al. suggested an authentication mechanism to provide anonymity and other security characteristics. We did the security analysis and informally proved that their scheme is prone to various attacks, such as failure to offer perfect forward secrecy, ephemeral secret leakage, traceability, replay, stolen device attacks, and also face desynchronization issues. These issues make the proposed scheme unsuitable for the healthcare system. Therefore, there is an impelling need to design an authentication mechanism that can restrict the attacker from getting any sensitive information. Considering the above requirements, we present a novel Zero Knowledge Proof based Authenticated Key Agreement (ZKP-AKA) protocol. The security of our proposed authentication mechanism is examined using the informal (non-mathematical) and formal (Scyther tool) security verification to confirm that the proposed protocol offers the prominent security features mentioned above. We also measure the performance to show that our proposed mechanism is suitable for IoT devices in the healthcare intelligent system by doing a comparative analysis with its competitors in terms of communication, computational, message exchange and energy consumption costs.

Multi-Tenant Architecture: A Comprehensive Framework for Building Scalable SaaS Applications

Multi-tenant architecture has emerged as a fundamental paradigm in modern software development, particularly in Software as a Service (SaaS) applications where multiple organizations share computing resources while maintaining data isolation. This article presents a comprehensive framework for understanding and implementing multi-tenant systems, focusing on essential architectural decisions and design patterns that ensure scalability, security, and resource efficiency. The article examines the evolution from single-tenant to multi-tenant architectures, analyzes various data partitioning strategies, and explores critical aspects of tenant isolation, authentication, and authorization mechanisms. The article addresses key challenges in performance optimization, resource allocation, and security implementation, providing practical insights into database design approaches and caching strategies. Through a structured approach, this article bridges the gap between theoretical concepts and practical implementation, offering developers and architects a foundation for building robust multi-tenant systems. The findings emphasize the importance of balanced architectural decisions that accommodate both technical requirements and business objectives while maintaining system integrity and tenant isolation. This article contributes to the growing body of knowledge in cloud computing and distributed systems, providing practitioners with actionable insights for developing scalable multi-tenant applications.

An efficient image encryption algorithm using 3D-cyclic chebyshev map and elliptic curve

With the development of internet technologies, it is now usual to communicate enormous amounts of text and visual data over networks, which calls for stringent procedures to guarantee confidentiality and integrity throughout transmission. An important part of safeguarding image communication over secure channel is cryptography. This study proposes an effective way to secure sensitive data by creating tamper-proof cryptosystems and authentication mechanisms. By employing elliptic curve cryptography (ECC) to generate secret encryption key and chaotic maps to successfully disguise and alter plain image pixel values, the technique strengthens overall system security through the use of hybrid cryptography. The method has a big enough key space to withstand brute-force attempts and exhibits robustness against statistical and differential attacks through analysis including evaluations and assessments of correlation, entropy, and histograms as well as evaluations such as NPCR, UACI, PSNR and keyspace analysis validate the efficacy of the proposed image encryption algorithm, affirming its systematic and robust nature and its capacity to offer superior image encryption protection over some existing ones.

Cross-layer Authentication Mechanism Model Combined with 5G Converged Channel Fingerprint

In the actual communication environment, once the attacker successfully steals the legitimate channel information, the key information can be cracked according to the authentication response, so there is a security risk of key leakage. This paper combines the physical layer channel characteristics with the shared key, and combines it into a joint key to design a challenge-response physical layer authentication mechanism based on interpolation polynomial. Then, this method is applied to the EAP-AKA’ authentication protocol of 5G network, and a cross-layer authentication mechanism for 5G converged channel fingerprint is proposed. Finally, using the captured high-level authentication challenge response data, a simulation environment is built in MATLAB and the feasibility and security of the scheme are verified. The experimental results show that the authentication mechanism has better authentication performance and security performance. Compared with the traditional high-level authentication mechanism, it has certain advantages in computing overhead and can meet the 5G application scenarios of large-scale IoT terminals. Moreover, it combines physical layer authentication with high-level authentication, realizes mutual complementarity and mutual enhancement, and enhances the security performance of authentication.

API Common Security Threats and Security Protection Strategies

This study analyzes the core role of APIs in modern digital ecology and the security threats they face, such as information leakage and overstepping access, and explores their security risks for technologies such as RESTful and GraphQL. It proposes to use OAuth/JWT authentication mechanism to strengthen access control, adopt HTTPS/TLS to secure data transmission, and combine with API gateway to defend against DDoS attacks. It also emphasizes the importance of fine-grained privilege management and log auditing. The study provides strategic guidance for improving API security protection and looks forward to the trend of intelligent protection.

Blockchain Technology as a Security Enhancement Framework for Internet of Things: A Comprehensive Analysis

The exponential growth of Internet of Things (IoT) devices has introduced significant security challenges that traditional cybersecurity measures struggle to address effectively. This research presents a comprehensive analysis of blockchain technology's integration with IoT systems as a solution to enhance security, privacy, and trust in connected device ecosystems. Through systematic evaluation of implementation architectures, security frameworks, and real-world applications, this study demonstrates how blockchain's inherent characteristics of decentralization, immutability, and cryptographic security can effectively address critical IoT vulnerabilities. The research examines specific applications across supply chain management, healthcare systems, and smart city infrastructure, providing quantitative and qualitative evidence of security improvements. While acknowledging challenges in scalability, energy consumption, and regulatory compliance, the study presents innovative solutions and future directions, including AI integration and privacy-enhancing technologies. The findings indicate that blockchain-IoT integration significantly improves data integrity, device authentication, and access control mechanisms, reducing security incidents by up to 60% in implemented cases. This research contributes to the growing body of knowledge on secure IoT architectures and provides practical insights for organizations considering blockchain implementation in their IoT security frameworks.