Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

Fei Tang^1,*, Ping Wang¹, Jiang Yu¹, Huihui Zhu¹, Mengxue Qin¹, Ling Yang²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073423 - 12 January 2026

Abstract Attribute-Based Encryption (ABE) has emerged as a fundamental access control mechanism in data sharing, enabling data owners to define flexible access policies. A critical aspect of ABE is key revocation, which plays a pivotal role in maintaining security. However, existing key revocation mechanisms face two major challenges: (1) High overhead due to ciphertext and key updates, primarily stemming from the reliance on revocation lists during attribute revocation, which increases computation and communication costs. (2) Limited universality, as many attribute revocation mechanisms are tailored to specific ABE constructions, restricting their broader applicability. To address these challenges,… More >

Ahmed Awad Mohamed¹, Eslam Abdelhakim Seyam^2,*, Ahmed R. Elsaeed³, Laith Abualigah⁴, Aseel Smerat^5,6, Ahmed M. AbdelMouty⁷, Hosam E. Refaat⁸

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073171 - 12 January 2026

Abstract In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across… More >

Anis Zarrad¹, Thomas Armstrong², Jaber Jemai^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072768 - 12 January 2026

Abstract Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability. While prioritization selects the most relevant test cases for optimal coverage, ranking further refines their execution order to detect critical faults earlier. This study investigates machine learning techniques to enhance both prioritization and ranking, contributing to more effective and efficient testing processes. We first employ advanced feature engineering alongside ensemble models, including Gradient Boosted, Support Vector Machines, Random Forests, and Naive Bayes classifiers to optimize test case prioritization, achieving an accuracy score of 0.98847… More >

José Arturo Ramírez-Fernández¹, Henevith G. Méndez-Figueroa¹, Sebastián Ossandón^2,*, Ricardo Galván-Martínez³, Miguel Ángel Hernández-Pérez³, Ricardo Orozco-Cruz³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072707 - 12 January 2026

Abstract In this study, artificial neural networks (ANNs) were implemented to determine design parameters for an impressed current cathodic protection (ICCP) prototype. An ASTM A36 steel plate was tested in 3.5% NaCl solution, seawater, and NS4 using electrochemical impedance spectroscopy (EIS) to monitor the evolution of the substrate surface, which affects the current required to reach the protection potential (Eprot). Experimental data were collected as training datasets and analyzed using statistical methods, including box plots and correlation matrices. Subsequently, ANNs were applied to predict the current demand at different exposure times, enabling the estimation of electrochemical More >

Abdul Rahaman Wahab Sait^1,*, Yazeed Alkhurayyif²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072481 - 12 January 2026

Abstract Malware poses a significant threat to the Internet of Things (IoT). It enables unauthorized access to devices in the IoT environment. The lack of unique architectural standards causes challenges in developing robust malware detection (MD) models. The existing models demand substantial computational resources. This study intends to build a lightweight MD model to detect anomalies in IoT networks. The authors develop a transformation technique, converting the malware binaries into images. MobileNet V2 is fine-tuned using improved grey wolf optimization (IGWO) to extract crucial features of malicious and benign samples. The ResNeXt model is combined with… More >

Muhammad Ejaz¹, Muhammad Asim^2,*, Mudasir Ahmad Wani^2,3, Kashish Ara Shakil^4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072464 - 12 January 2026

Abstract The integration of High-Altitude Platform Stations (HAPS) with Reconfigurable Intelligent Surfaces (RIS) represents a critical advancement for next-generation wireless networks, offering unprecedented opportunities for ubiquitous connectivity. However, existing research reveals significant gaps in dynamic resource allocation, joint optimization, and equitable service provisioning under varying channel conditions, limiting practical deployment of these technologies. This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning (FAIR-DQL) framework for joint resource management and phase configuration in HAPS-RIS systems. Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control, priority-based user scheduling, and dynamic learning mechanisms. More >

Shuangqing Song¹, Yuan Chen², Xuguang Hu¹, Juwei Zhang^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072392 - 12 January 2026

Abstract In multi-domain neural machine translation tasks, the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains. This paper proposes a Transformer-based multi-domain-aware mixture of experts model. To address the problem of domain feature differentiation, a mixture of experts (MoE) is introduced into attention to enhance the domain perception ability of the model, thereby improving the domain feature differentiation. To address the trade-off between domain feature distinction and cross-domain parameter sharing, we propose a domain-aware mixture of experts (DMoE). A domain-aware gating mechanism is introduced within the… More >

Haotian Zhang, Jing Li^*, Ming Zhu, Zhiyong Zhao, Hongli Su, Liming Sun

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072381 - 12 January 2026

Abstract In the wireless energy transmission service composition optimization problem, a key challenge is accurately capturing users’ preferences for service criteria under complex influencing factors, and optimally selecting a composition solution under their budget constraints. Existing studies typically evaluate satisfaction solely based on energy transmission capacity, while overlooking critical factors such as price and trustworthiness of the provider, leading to a mismatch between optimization outcomes and user needs. To address this gap, we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios, systematically incorporating service price, transmission capacity, and trustworthiness into the satisfaction assessment… More >

Olanrewaju Lawrence Abraham^1,2,*, Md Asri Ngadi¹, Johan Bin Mohamad Sharif¹, Mohd Kufaisal Mohd Sidik¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072279 - 12 January 2026

Abstract Task scheduling in cloud computing is a multi-objective optimization problem, often involving conflicting objectives such as minimizing execution time, reducing operational cost, and maximizing resource utilization. However, traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems. To address this limitation, we introduce MDMOSA (Multi-objective Dwarf Mongoose Optimization with Simulated Annealing), a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service (IaaS) cloud environments. MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization (DMO) with the exploitation strengths of Simulated Annealing (SA), More >