A Multifactor Authentication Framework for Usability in Education Sectors in Uganda

- The growth of Internet Service Providers (ISPs) has been catalyzed by customizable platforms that allow white-labeling, enabling resellers to brand services under their identity. This paper investigates the technical and UX challenges involved in white-labeled ISP platforms, focusing on scalability, UI consistency, branding freedom, and user management. We explore architectural decisions behind cloud-based white-label systems, user experience design for multi-brand interfaces, and the challenges faced in maintaining a balance between flexibility and security. The paper proposes a system design and usability framework to optimize white-labeled deployments in hospitality, enterprise, education, and rural broadband sectors. Findings are based on qualitative UX evaluations and infrastructure simulations, demonstrating that intentional design principles significantly enhance end-user satisfaction and operational manageability. Key Words: White-label, ISP, UI/UX, cloud management, multi-tenancy, network platforms.

This paper introduces Cloud Safety, a robust multi-factor authentication (MFA) framework that integrates password-based authentication, Time-based One-Time Password (TOTP), and SMS OTP to enhance identity assurance in cloud environments.The framework addresses critical vulnerabilities in traditional single-factor systems-such as phishing, brute force, and replay attacks-by implementing layered security controls without compromising usability.Additionally, encryption mechanisms are applied at the data layer to protect stored information.Designed for real-world deployment, Cloud Safety emphasizes low-latency performance, scalability, and adaptability to modern threat models.Comparative analysis and implementation results demonstrate its effectiveness in mitigating common cloud security threats while maintaining a practical balance between security and user experience.Future enhancements may include biometric integration and adaptive, AI-driven access control.

A lightweight three factor authentication framework for IoT based critical applications

In this paper we present UAuth, a two-layer authentication framework that provides more security assurances than two-factor authentication while offering a simpler authentication experience. When authenticating, users first verified their static credentials (such as password, fingerprint, etc.) in the local layer, then submit the OTP-signed response generated by their device to the server to complete the server-layer authentication. We also propose the three-level account association mechanism, which completes the association of devices, users and services, establishing a mapping from a user’s device to the user’s accounts in the Internet. Users can easily gain access to different service via a single personal device. Our goal is to provide a quick and convenient SSO-like login process on the basis of security authentication. To meet the goal, we implement our UAuth, and evaluate our designs.

Authentication is of paramount significance to cybersecurity. However, most of conventional authentication schemes are implemented in a centralized mode, in which potential problems that could arise include single-point failure, the exposure of personal information, and the risk of identity theft. Additionally, static single-factor authentication schemes are unsuitable for dynamic environments like mobile applications. In order to tackle these difficulties, we propose a blockchain-based authentication scheme with an adaptive multi-factor authentication strategy. Our scheme features a blockchain-based authentication framework that prevents unauthorized information alteration and system corruption. Additionally, we design an adaptive multi-factor authentication strategy model to ensure trustworthy multi-factor authentication in dynamic scenarios. Last, we construct a Raft-based consensus model to select an authoritative leading node for rapid authentication. The security analysis demonstrates the effectiveness of the proposed scheme in effectively countering various forms of cyberattacks targeted at authentication systems, and experiments demonstrate its superior effectiveness and efficiency compared to existing studies.

With the rapid development of the Internet, the application of the Internet of things and big data is more and more extensive. The era of Internet of everything (IoE) has come, and the traditional cloud computing model has been unable to efficiently process the massive data generated by a large number of edge devices. Therefore, edge-type big data processing which is oriented to massive data computing generated by network edge devices—edge computing comes into being. However, due to the complexity of edge computing, data security and privacy issues become more prominent. Aiming at the security authentication of edge equipment under the Internet of everything, this paper designs an identity authentication framework under the Internet of everything based on edge computing. In the framework, multi-factor identity authentication is applied to solve the weakness of edge equipment security authentication. Moreover, the software defined network technology (SDN) is adopted to realize the global management of the deployment and application of a large number of edge equipment, which can effectively realize the effective security protection of the Internet of everything. In the end, the formalized verification of the identity authentication process of the designed framework is carried out.

In 2009, Xu et al. presented an improved smartcard based authentication scheme while using a security model previously applied by Bellare et al. to prove the security of their authentication methods. Later on, in 2012, Wu et al. pointed out number of authentication attacks in Xu et al. scheme. To address these issues, Wu et al. presented a Smartcard based Two-Factor Authentication (2FA) scheme for Telecare Medical Information System (TMIS) facility. In this study, we prove that authentication scheme of Wu et al. is still vulnerable to impersonation attack, offline password guessing attack, forgery attack and many other attacks. Moreover, number of performance and verification issues are also outlined in the authentication scheme of Wu et al. To overcome these issues, an improved and enhanced 3FA Smartphone based authentication method is proposed on a Cloud Computing environment. The proposed scheme is further corroborated using Burrows-Abadi-Needham logic (BAN logic) nonce verification. The detailed BAN logic verification and further security analysis shows that the proposed authentication protocol is highly reliable and secure in terms of message verifications, message freshness and trustworthiness of its origin. Moreover, the comparative security, performance and feature analysis shows that the proposed work yields an even more improved and enhanced authentication framework as compared to Wu et al. authentication scheme.

The social Internet of Medical Things (S-IoMT) highly demands dependable and non-invasive device identification and authentication and makes data services more prevalent in a reliable learning system. In real time, healthcare systems consistently acquire, analyze, and transform a few operational intelligence into actionable forms through digitization to capture the sensitive information of the patient. Since the S-IoMT tries to distribute health-related services using IoT devices and wireless technologies, protecting the privacy of data and security of the device is so crucial in any eHealth system. To fulfill the design objectives of eHealth, smart sensing technologies use built-in features of social networking services. Despite being more convenient in its potential use, a significant concern is a security preventing potential threats and infringement. Thus, this paper presents a lightweight two-factor authentication framework (L2FAK) with privacy-preserving functionality, which uses a mobile sink for smart eHealth. Formal and informal analyses prove that the proposed L2FAK can resist cyberattacks such as session stealing, message modification, and denial of service, guaranteeing device protection and data integrity. The learning analysis verifies the features of the physical layer using federated learning layered authentication (FLLA) to learn the data characteristics by exploring the learning framework of neural networks. In the evaluation, the core scenario is implemented on the TensorFlow Federated framework to examine FLLA and other relevant mechanisms on two correlated datasets, namely, MNIST and FashionMNIST. The analytical results show that the proposed FLLA can analyze the protection of privacy features effectively in order to guarantee an accuracy ≈89.83% to 93.41% better than other mechanisms. Lastly, a real-time testbed demonstrates the significance of the proposed L2FAK in achieving better quality metrics, such as transmission efficiency and overhead ratio than other state-of-the-art approaches.

Cloud computing an emerging computing model having its roots in grid and utility computing is gaining increasing attention of both the industry and laymen. The ready availability of storage, compute, and infrastructure services provides a potentially attractive option for business enterprises to process and store data without investing on computing infrastructure. The attractions of Cloud are accompanied by many concerns among which Data Security is the one that requires immediate attention. Strong user authentication mechanisms which prevent illegal access to Cloud services and resources are one of the core requirements to ensure secure access. This paper proposes a user authentication framework for Cloud which facilitates authentication by individual service providers as well as by a third party identity provider. The proposed two-factor authentication protocols uses password as the first factor and a Smart card or Mobile Phone as the second factor. The protocols are resistant to various known security attacks.

Advancements in artificial intelligence (AI), the Internet of Things (IoT), and cloud computing have paved the way for transformative technologies in the healthcare industry. This report focuses on the development of an AI-Enabled Mobile Cloud-Based Federated IoMT Architecture for ambient-assisted healthy living. The objective is to leverage the power of AI, IoT, and cloud computing to create intelligent and context-aware healthcare solutions that promote healthy living in personalized and efficient ways. The integration of AI with the Internet of Medical Things (IoMT) enables the creation of ambient-assisted living (AAL) environments [1], which utilize IoT devices and sensors to support individuals in their daily routines. Real-time data collected from these IoT devices facilitates personalized healthcare services, enabling individuals to live independently and safely. To ensure secure and efficient operation, lightweight two-factor authentication frameworks with privacy preservation for mobile sinks in the social IoMT have been investigated. These frameworks aim to protect sensitive healthcare data transmitted through the IoMT ecosystem. IoT architectures based on microservices have been explored for AAL environments, aiming to provide personalized assistance and healthcare support to seniors.

The fast-emerging of cloud computing technology today has sufficiently benefited its wide range of users from individuals to large organizations. It carries an attractive characteristic by renting myriad virtual storages, computing resources and platform for users to manipulate their data or utilize the processing resources conveniently over Internet without the need to know the exact underlying infrastructure which is resided remotely at cloud servers. However due to the loss of direct control over the systems/applications, users are concerned about the risks of cloud services if it is truly secured. In the literature, there are cases where attackers masquerade as cloud users, illegally access to their accounts, by stealing the static login password or breaking the poor authentication gate. In this paper, we propose a two-factor authentication framework to enforce cloud services' authentication process, which are Public Key Infrastructure (PKI) authentication and mobile out-of-band (OOB) authentication. We discuss the framework's security analysis in later session and conclude that it is robust to phishing and replay attacks, prohibiting fraud users from accessing to the cloud services.

Security issues in cloud computing is one of the major reasons in slowing down its adoption by businesses and among these issues: weak user authentication. In fact, the use of text password is still relatively widespread, especially, for authentication in a cloud environment. Therefore, numerous approaches have been proposed to overcome this problem such as graphical passwords, biometric scans and 3D password objects. In this paper, we propose a cloud-based two-factor authentication framework called Two Levels Authentication Cloud (2LAC). The framework uses an image and a secret watermark as factors of authentication. Experimental results illustrate that 2LAC offers an efficient mechanism for enhancing user authentication in the cloud environment and can eliminate common types of attacks.

A lightweight machine learning-based authentication framework for smart IoT devices

The creation of the National Health Insurance Scheme (NHIS) in 2005 to replace the traditional “cash and carry” healthcare financial model, was anticipated to offer a safe, reliable, affordable and national coverage healthcare system for the Ghanaian populace. The scheme has recorded several challenges; as a result, policymakers and donor agencies are reconsidering the current NHIS model and are thinking of crafting a better alternate and sustainable financial model for the NHIS. This study seeks to propose a multifactor authentication framework for the national health insurance scheme in Ghana using soft-computing machine learning techniques to minimize the current challenges. It was observed that the proposed system used 1.02 sec to vet 25 claim forms, while the human professional used 120 sec for a single document. The accuracy (91.50%) and F1 (88.52) score measure obtained shows a higher rate of the vetting process by the proposed system.

Different services are provided via cloud computing over the Internet to benefit Small and Medium Enterprises (SMEs). In order to suit client needs, the cloud computing environment consists of hardware and software resources housed in data centres that provide a range of services across a network or the Internet. However, due to the difficulties in adopting cloud computing, SMEs are unable to use the technology extensively. In cloud-based contexts, authentication security issues are the most significant obstacles. The security of the cloud environment’s current authentication technique is geared toward massive enterprises and organizations. Therefore, it would not be cost-effective for SMEs to adopt the suggested multifactor authentication(MFA) framework. Single sign-on (SSO) is the basis for the authentication security provided and is vulnerable to several cyber-attacks. This study determines the necessary requirements for developing the framework for multifactor authentication requirements for the cloud computing environment from an SME perspective. This study uses a systematic literature review and preliminary investigation with experts from SMEs to identify the required components. The study will benefit SMEs using cloud computing and elevate many SMEs’ operational levels to minimize the cost and additional resources. Additionally, the increased demand for cloud computing services explains the requirement for SME businesses to have cloud services that are more efficient, safe, and well-maintained.