Joong-Hyun Park¹, Jiho Choi², Libor Mesicek³, Hoon Ko^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084659 - 23 July 2026

Abstract With the rapid expansion of the Internet of Medical Things (IoMT), the importance of digital identity–based security has significantly increased. However, conventional static authentication mechanisms are insufficient to effectively address various identity misuse and abuse attacks. In this study, we model digital identity as a dynamic security entity and propose an AI-based framework that integrates a risk scoring model—combining unsupervised anomaly detection with context-aware analysis—and a multi-level risk-adaptive access control mechanism (Permit, Step-Up, Restrict). Experimental results using an extended version of the CERT Insider Threat Dataset tailored for IoMT environments provide proof-of-concept evidence that the More >

Qiuhao Xu^1,2, Chen Wang^1,3,*, Xi Wen¹, Lurong Jiang¹, Wenying Zheng^4,*, Zhengkui Chen¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082310 - 23 July 2026

Abstract The convergence of Generative Artificial Intelligence and biometric recognition is reshaping modern healthcare. It enables more adaptive and intelligent human–machine interactions. Epilepsy, a common neurological disorder affecting millions worldwide, relies heavily on electroencephalography (EEG) signals for diagnosis and monitoring. Wearable consumer devices with EEG sensors support continuous physiological data collection. However, transmitting sensitive biometric data to centralized servers introduces serious privacy and security risks. Federated learning (FL) provides a distributed training framework that keeps raw data on local devices. Despite this advantage, existing FL methods remain vulnerable to gradient leakage attacks, where adversaries may infer More >

Rayan Alenzi, Rayan Aldoghan^*, M. M. Hafizur Rahman

Journal of Cyber Security, Vol.8, pp. 373-396, 2026, DOI:10.32604/jcs.2026.083953 - 01 July 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices across smart homes, healthcare facilities, industrial networks, and smart cities has raised critical security concerns, particularly regarding device authentication. IoT devices are typically characterized by limited computational resources, constrained memory, and restricted energy budgets, which renders the deployment of traditional cryptographic protocols infeasible; consequently, lightweight authentication schemes are required. Although numerous lightweight authentication protocols have been proposed, a systematic risk evaluation of such protocols against established threat modeling frameworks remains largely absent from the existing literature. This paper presents a systematic literature review (SLR) based on… More >

Sara Matheu^1,*, Pedro Ruzafa¹, Ilias Kalouptsoglou², Antonio Skarmeta¹, Dionysios Kehagias²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083308 - 30 June 2026

Abstract The evolution of the Future Mobile Internet, driven by large-scale connectivity and heterogeneous device ecosystems, introduces significant challenges for securely integrating devices into operational environments. Existing onboarding mechanisms primarily focus on authentication and credential provisioning, while security policy enforcement is typically deferred, creating a temporal gap during which devices may operate without appropriate constraints. This paper addresses this limitation by enabling policy enforcement during onboarding. To this end, we propose a model-driven approach that integrates the Device Security Passport (DSP) with the FIDO Device Onboard (FDO) protocol. The DSP is a lifecycle-aware model that aggregates… More >

M. Saad Bin Ilyas¹, Sohail Masood Bhatti¹, Ghazanfar Latif^2,*, Sherif Abdelhamid³, Arfan Jaffar¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081720 - 30 June 2026

Abstract Federated learning (FL) has emerged as a promising approach for enabling collaborative model training across distributed Internet of Medical Things (IoMT) devices without sharing sensitive data. Existing FL frameworks face significant challenges in healthcare settings, including vulnerability to adversarial attacks, lack of verifiable update integrity, and limited robustness under heterogeneous data distributions. These limitations hinder reliable deployment in critical medical applications. To address these challenges, this paper proposes RP-IoMT, a robust and privacy-preserving FL framework that integrates secure multi-party computation (MPC), zero-knowledge proof-based gradient verification, and robust aggregation mechanisms. The objective of this work is… More >

Akmalbek Abdusalomov¹, Kudratjon Zohirov², Sojida Ochilova², Jakhongir Oramov³, Zafar Ruziyev³, Malika Rustamova⁴, Gulrukh Sherboboyeva⁵, Komil Tashev^6,7, Young Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081292 - 15 June 2026

Abstract Unmanned aerial vehicles (UAVs) are also increasingly becoming more often in the transportation infrastructure of smart cities, so that they can successfully achieve real-time observation of traffic, emergency coordination, and two-way communication relaying. However, the security and privacy risks arising in open, highly mobile intelligent transportation systems (ITS) enabled by UAVs are critical, as they pose threats of impersonation, replay, Sybil, and tracking attacks. Secondly, standard static authentication mechanisms are unable to support dynamic risk environments and excessive resource consumption on UAV platforms with limited capacity. To address these challenges, this study introduces a Generative-AI-assisted… More >

Rifat Sarker Aoyon¹, Fahmid Al Farid^2,3, Ismail Hossain⁴, Mahe Zabin⁵, Sarina Mansor^2,*, Jia Uddin^6,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079831 - 15 June 2026

Abstract This research introduces an innovative lightweight image segmentation framework where models of hybrid architectures work together to predict the output and also have self-adapting ability, along with maintaining data privacy. In this framework, data is distributed and trained in a decentralized way using different deep learning architectures. That is how the advantages of all these models will be integrated into the system. Each trained model makes its own prediction, and the final output is determined through cooperation among these models. Here, the confidence-level and pixel-wise voting majority algorithms will be utilized for the co-operation-based output… More >

Tanveer Ahmad^1,*, Tahani Alsubait², Amina Salhi³, Amani Ibraheem⁴, Muhammad Asim Saleem⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080285 - 27 May 2026

Abstract The rapid adoption of Edge-AI in smart edge-IoT environments has dramatically led to an augmented vulnerability to cyber risks arising from distributed learning, data heterogeneity, and adversarial manipulation. This paper proposes a new risk-aware adaptive learning model that federated Edge-AI systems explicitly simulates cyber risk in the process of local training and global aggregation. The proposed solution combines stochastic optimization and adversarial risk bounding with adaptive gradient correction to develop strong learning in non-IID data distributions and malicious client behavior. Convergence guarantees are defined by the theoretical analysis in the case of limited adversarial perturbations.… More >

Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026

Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >

Chirag Devendrakumar Parikh^*

Journal on Internet of Things, Vol.8, pp. 109-125, 2026, DOI:10.32604/jiot.2026.077412 - 12 May 2026

Abstract Secure IoT ecosystems are based on the notion that device authentication is reputable. Traditional approaches typically use software identifiers or stored cryptographic keys, which can be cloned, copied, or modified by physical access or supply-chain interference. The current paper presents a hardware fingerprinting system that is based on compliance to enhance the strength of the authentication of the IoT device, that is, to connect physical device properties with organized conformity practices. The tool exploits intrinsic electrical and manufacturing differences in parts to produce device-specific fingerprints and compares these fingerprints with compliance processes, including component validation, More >