I Wayan Adi Juliawan Pawana^1,2, Vincent Abella², Jhury Kevin Lastre², Yongho Ko², Ilsun You^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2733-2760, 2025, DOI:10.32604/cmes.2025.072611 - 26 November 2025

Abstract Roaming in 5G networks enables seamless global mobility but also introduces significant security risks due to legacy protocol dependencies, uneven Security Edge Protection Proxy (SEPP) deployment, and the dynamic nature of inter-Public Land Mobile Network (inter-PLMN) signaling. Traditional rule-based defenses are inadequate for protecting cloud-native 5G core networks, particularly as roaming expands into enterprise and Internet of Things (IoT) domains. This work addresses these challenges by designing a scalable 5G Standalone testbed, generating the first intrusion detection dataset specifically tailored to roaming threats, and proposing a deep learning based intrusion detection framework for cloud-native environments.… More > Graphic Abstract

Mohammad A Al Khaldy¹, Ahmad Nabot², Ahmad Al-Qerem^3,*, Mohammad Alauthman⁴, Amina Salhi^5,*, Suhaila Abuowaida⁶, Naceur Chihaoui⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 673-697, 2025, DOI:10.32604/cmes.2025.070004 - 30 October 2025

Abstract The exponential growth of Internet of Things (IoT) devices has created unprecedented challenges in data processing and resource management for time-critical applications. Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems, while pure edge computing faces resource constraints that limit processing capabilities. This paper addresses these challenges by proposing a novel Deep Reinforcement Learning (DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments. Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency. The framework introduces three key innovations: (1)… More >

Zeinel Momynkulov^1,2, Azhar Tursynova^1,2,*, Olzhas Olzhayev^1,2, Akhanseri Ikramov^1,2, Sayat Ibrayev¹, Batyrkhan Omarov^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 891-918, 2025, DOI:10.32604/cmes.2025.068615 - 30 October 2025

Abstract Robotic manipulators increasingly operate in complex three-dimensional workspaces where accuracy and strict limits on position, velocity, and acceleration must be satisfied. Conventional geometric planners emphasize path smoothness but often ignore dynamic feasibility, motivating control-aware trajectory generation. This study presents a novel model predictive control (MPC) framework for three-dimensional trajectory planning of robotic manipulators that integrates second-order dynamic modeling and multi-objective parameter optimization. Unlike conventional interpolation techniques such as cubic splines, B-splines, and linear interpolation, which neglect physical constraints and system dynamics, the proposed method generates dynamically feasible trajectories by directly optimizing over acceleration inputs while… More >

Siraj Un Muneer¹, Ihsan Ullah¹, Zeshan Iqbal^2,*, Rajermani Thinakaran³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4959-4975, 2025, DOI:10.32604/cmc.2025.068566 - 23 October 2025

Abstract The complexity of cloud environments challenges secure resource management, especially for intrusion detection systems (IDS). Existing strategies struggle to balance efficiency, cost fairness, and threat resilience. This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm (GA) with a “double auction” method. This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework. It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders. The GA functions as an intelligent search mechanism that identifies optimal combinations of bids More >

Xuezhi Wen^1,2, Eric Danso^1,2,*, Solomon Danso¹

Journal of Cyber Security, Vol.7, pp. 439-462, 2025, DOI:10.32604/jcs.2025.070587 - 17 October 2025

Abstract Cloud-based intrusion detection systems increasingly face sophisticated adversarial attacks such as evasion and poisoning that exploit vulnerabilities in traditional machine learning (ML) models. While deep learning (DL) offers superior detection accuracy for high-dimensional cloud logs, it remains vulnerable to adversarial perturbations and lacks interpretability. Conversely, Hidden Markov Models (HMMs) provide probabilistic reasoning but struggle with raw, sequential cloud data. To bridge this gap, we propose a Deep Learning-Enhanced Ensemble Hidden Markov Model (DL-HMM) framework that synergizes the strengths of Long Short-Term Memory (LSTM) networks and HMMs while incorporating adversarial training and ensemble learning. Our architecture… More >

Victor Otieno Mony^*, Anselemo Peters Ikoha, Roselida O. Maroko

Journal of Cyber Security, Vol.7, pp. 395-415, 2025, DOI:10.32604/jcs.2025.070952 - 30 September 2025

Abstract The widespread adoption of Cloud-Based Services has significantly increased the surface area for cyber threats, particularly targeting authentication mechanisms, which remain among the most vulnerable components of cloud security. This study aimed to address these challenges by developing and evaluating an Intelligent Zero Trust Architecture model tailored to mitigate authentication-related threats in Cloud-Based Services environments. Data was sourced from public repositories, including Kaggle and the National Institute for Standards and Technology MITRE Corporation’s Adversarial Tactics, Techniques, & Common Knowledge (ATT&CK) framework. The study utilized two trust signals: Behavioral targeting system users and Contextual targeting system… More >

Reza Nadimi^1,*, Maryam Hashemi², Koji Tokimatsu³

Journal on Internet of Things, Vol.7, pp. 49-69, 2025, DOI:10.32604/jiot.2025.070822 - 30 September 2025

Abstract Existing Internet of Things (IoT) systems that rely on Amazon Web Services (AWS) often encounter inefficiencies in data retrieval and high operational costs, especially when using DynamoDB for large-scale sensor data. These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems. This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services. The proposed method employs AWS Lambda functions with Amazon Relational Database Service (RDS) to facilitate the transmission of data collected from temperature and humidity sensors to the… More >

Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025

Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >

Guoqiang Long^*, Wenling Chen, Yanghui Ou, Shuting Yang

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010487

Abstract China's marine carbonate rocks are widely distributed, the reservoir is deeply buried, high temperature, high closure pressure, and the reservoir has strong non-homogeneity, porosity and permeability are generally low, while the natural cracks and dissolution pore (hole) is more developed. Currently, carbonate reservoir reforming technology is developing rapidly, and more and more marine carbonates can be developed. The Permian Maokou-Qixia Formation in the Sichuan Basin has good hydrocarbon source rocks of marine carbonates. The Longniusi Hechuan block of Permian Maokou-Qixia Formation develops a set of carbonate reservoirs interacting with leopard dolomite and mud crystal clastic… More >

Ray-I Chang¹, Chia-Hui Wang^2,*, Yen-Ting Chang¹, Lien-Chen Wei²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 503-518, 2025, DOI:10.32604/cmc.2025.068516 - 29 August 2025

Abstract As data analysis often incurs significant communication and computational costs, these tasks are increasingly outsourced to cloud computing platforms. However, this introduces privacy concerns, as sensitive data must be transmitted to and processed by untrusted parties. To address this, fully homomorphic encryption (FHE) has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service (MLaaS), enabling computation on encrypted data without revealing the plaintext. Nevertheless, FHE remains computationally expensive. As a result, approximate homomorphic encryption (AHE) schemes, such as CKKS, have attracted attention due to their efficiency. In our previous work, we proposed RP-OKC, a CKKS-based clustering… More >