Ji Su Park^1,*, Pan Yi², Jong Hyuk (James) Park³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083347 - 27 May 2026

Abstract This article has no abstract. More >

Alanoud Al Mazroa¹, Fahad Masood², Bakri Hussain Awaji³, Mohammad Alhefdi⁴, Abeer Aljohani⁵, Jawad Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080134 - 27 May 2026

Abstract The rapid integration of Internet of Things (IoT) devices and distributed energy resources into smart grids has improved monitoring, control, and energy efficiency. However, it also exposes the grid to cyberattacks and privacy risks, as increased connectivity and data exchange can significantly disrupt energy management and system stability. Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks. This study proposes a privacy-preserving and cyber-resilient energy-optimization framework, FedGNN, for IoT-enabled smart grids that jointly integrates federated learning, graph neural network-based trust inference, and trust-aware energy dispatch.… More >

Ghadi Almaktoom, Suliman Aladhadh, Salim El Khediri^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078774 - 27 May 2026

Abstract The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy. The primary focus of the research is on detecting phishing attacks, with no interest in how the data is processed. Attackers use fraudulent methods to obtain valuable, confidential information, resulting in billions of dollars in financial losses for enterprises. In our review, we examined the methods used in phishing-detection studies. We concluded that the two main sections, centralized and decentralized methods, were the centralized ones, which aggregate data in a central server and thus violate data protection regulations, such as GDPR.… More >

Tao Bai¹, Yang Tang², Kuan Shao³, Zhenyong Zhang^3,*, Yuanteng Liu⁴

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078473 - 08 May 2026

Abstract In recent years, the rapid development of artificial intelligence has greatly promoted the application of Machine Learning as a Service (MLaaS). Users can upload their requirements through front-end applications, and the server provides model inference services after receiving the user input. However, MLaaS may lead to serious privacy breaches. Large language model services are typical representatives of MLaaS, and the Transformer is a typical structure in large language models. Therefore, this paper proposes a privacy-protected Transformer inference scheme based on the CKKS fully homomorphic encryption scheme to optimize computational and communication efficiency. Firstly, this paper… More >

Muhammet Ali Karabulut¹, A. F. M. Shahen Shah², Al-Sakib Khan Pathan^3,*, Phillip G. Bradford⁴

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077625 - 08 May 2026

Abstract Today, technological progress is broad and deep. The next generation networks and systems will integrate features, technologies, and models requiring smooth cooperation between new and old technologies. This survey’s uniqueness is that it considers an integrated, hybrid and heterogeneous future where Internet of Things (IoT), Sixth-Generation (6G) mobile communications technology, and Artificial Intelligence (AI) will work together, providing a smart and connected Intelligent Transportation System (ITS). This smart ITS will give better road safety and optimized travel. Currently, there is a scarcity of surveys focusing particularly on smart ITS that is expected soon. In this More >

Oluwatosin Ahmed Amodu¹, Zurina Mohd Hanapi^1,*, Raja Azlina Raja Mahmood¹, Faten A. Saif², Huda Althumali³, Chedia Jarray⁴, Umar Ali Bukar⁵, Mohammed Sani Adam⁶

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.074678 - 08 May 2026

Abstract Non-terrestrial networks (NTNs), encompassing unmanned aerial vehicles (UAVs), low-/high-altitude platforms (LAPs/HAPs), and satellite systems, are increasingly enabling Internet of Things (IoT) applications beyond the limits of terrestrial infrastructure. By combining UAV mobility with satellite and HAP coverage, NTN-assisted IoT supports diverse use cases, including remote sensing, smart cities, intelligent transportation, and emergency response. This paper presents a systematic mapping of machine learning (ML) research in NTN-assisted IoT with a focus on security-related aspects. A keyword co-occurrence analysis of over 2000 publications identifies twelve thematic clusters, including three clusters directly related to security, privacy, and trust.… More >

Nomangwane Angelina Tshabalala¹, Ping Guo^2,*

Journal on Internet of Things, Vol.8, pp. 87-107, 2026, DOI:10.32604/jiot.2026.078248 - 06 May 2026

Abstract Type 2 Diabetes mellitus is a disease that afflicts approximately 537 million individuals all over the world, and continuous glucose monitoring (CGM) systems have become very important in the management of the disease. Nonetheless, the existing centralized data architecture of CGM generates high privacy and security risks, as sensitive patient health data can be easily abused. This paper introduces an original structure that incorporates both federated learning and blockchain technology and allows for predicting glucose safely and preserving privacy without affecting the integrity of the data. Our model uses the Long Short-Term Memory (LSTM) neural… More >

Maram Fahaad Almufareh^1,*, Mamoona Humayun², Sadia Din^3,*, Khalid Haseeb⁴, Amr Munshi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080287 - 27 April 2026

Abstract The real-time systems perform key functionalities in various fields to automate the communication and response in critical events. The Internet of Things (IoT), integrated with numerous physical objects, gathers environmental data, processes it at the edge, and provides intelligent decisions while routing health records to processing units. However, the dynamic and resource-constrained nature of IoT-based healthcare environments introduces significant challenges related to latency, transmission costs, and the reliable interaction of devices amid uncertain activities. In this work, we propose a framework for a consistent and trustworthy system that uses a weighted trust aggregation model to More >

Saeideh Memarian¹, Andreea M. Oprescu^2,3, Natalia Moreno-Naranjo², Gloria Miró-Amarante², M. Carmen Romero-Ternero^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.073647 - 27 April 2026

Abstract In recent years, the proliferation of social networks has been remarkable, providing a rich source for data mining endeavors. However, a significant challenge lies in safeguarding the privacy of individuals while sharing these databases publicly. Current approaches, such as K-anonymity, L-diversity, and T-closeness, are commonly employed for data anonymization in social networks. However, these techniques entail considerable information loss due to random alterations in the graph-based datasets. To address these limitations, this paper introduces a new anonymization technique called KMFC-GWO, which combines K-Member Fuzzy Clustering with Grey Wolf Optimizer. This integrated method is designed to… More >

Di Xiao^*, Wenting Jiang, Min Li

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079808 - 09 April 2026

Abstract The rapid advancement of the Internet of Things (IoT) has transformed edge devices from simple data collectors into intelligent units capable of local processing and collaborative learning. However, the vast amounts of sensitive data generated by these devices face severe constraints from “data silos” and risks of privacy breaches. Federated learning (FL), as a distributed collaborative paradigm that avoids sharing raw data, holds great promise in the IoT domain. Nevertheless, it remains vulnerable to gradient leakage threats. While traditional differential privacy (DP) techniques mitigate privacy risks, they often come at the cost of significantly reduced… More >