Nader Karmous¹, Wadii Jlassi¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4363-4393, 2025, DOI:10.32604/cmes.2025.074178 - 23 December 2025

Abstract This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments, built upon a novel, synthetic multi-vector dataset generated in a Mininet-Ryu testbed using real-time flow-based labeling. The proposed model is based on the XGBoost algorithm, optimized with Principal Component Analysis for dimensionality reduction, utilizing lightweight flow-level features extracted from OpenFlow statistics to classify attacks across critical IoT protocols including TCP, UDP, HTTP, MQTT, and CoAP. The model employs lightweight flow-level features extracted from OpenFlow statistics to ensure low computational overhead and fast processing. Performance was rigorously… More >

Ana Hermosilla^1,2,*, Pedro Martinez-Julia³, Diego R. Lopez⁴, Antonio F. Skarmeta^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4279-4316, 2025, DOI:10.32604/cmes.2025.072607 - 23 December 2025

Abstract In this study, we analyzed the processes involved in the resolution and enforcement of multi-domain network intent policies for intent-based networking (IBN). Previous studies on IBN analyzed the basis of the network intent resolution processes. These processes produce the artifacts required by network intent policy enforcement. Thus, we continued such studies with the inclusion of network intent policy enforcement in the analysis, for which we constructed a model that predicts the accuracy of a multi-domain network intent policy enforcement system. We validated the model by designing a new multi-domain network intent policy enforcement system, and… More >

Somayah Albaradei^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2937-2970, 2025, DOI:10.32604/cmes.2025.072584 - 23 December 2025

Abstract Cancer deaths and new cases worldwide are projected to rise by 47% by 2040, with transitioning countries experiencing an even higher increase of up to 95%. Tumor severity is profoundly influenced by the timing, accuracy, and stage of diagnosis, which directly impacts clinical decision-making. Various biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, contribute to cancer development. The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers. This integrative approach supports the notion that cancer is fundamentally driven by such alterations, enabling the discovery of molecular signatures… More > Graphic Abstract

Mekhled Alharbi^1,*, Khalid Haseeb², Mamoona Humayun^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2601-2616, 2025, DOI:10.32604/cmes.2025.073039 - 26 November 2025

Abstract Emerging technologies and the Internet of Things (IoT) are integrating for the growth and development of heterogeneous networks. These systems are providing real-time devices to end users to deliver dynamic services and improve human lives. Most existing approaches have been proposed to improve energy efficiency and ensure reliable routing; however, trustworthiness and network scalability remain significant research challenges. In this research work, we introduce an AI-enabled Software-Defined Network (SDN)- driven framework to provide secure communication, trusted behavior, and effective route maintenance. By considering multiple parameters in the forwarder selection process, the proposed framework enhances network More >

Zesheng Xi^1,2,#, Chuan He^1,3,#, Yunfan Wang^1,3,#, Bo Zhang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2545-2565, 2025, DOI:10.32604/cmes.2025.066822 - 31 August 2025

Abstract The increasing reliance on interconnected Internet of Things (IoT) devices has amplified the demand for robust anonymization strategies to protect device identities and ensure secure communication. However, traditional anonymization methods for IoT networks often rely on static identity models, making them vulnerable to inference attacks through long-term observation. Moreover, these methods tend to sacrifice data availability to protect privacy, limiting their practicality in real-world applications. To overcome these limitations, we propose a dynamic device identity anonymization framework using Moving Target Defense (MTD) principles implemented via Software-Defined Networking (SDN). In our model, the SDN controller periodically… More >

Wenlong Ke^1,2,*, Zilong Li¹, Peiyu Chen¹, Benfeng Chen¹, Jinglin Lv¹, Qiang Wang², Ziyi Jia², Shigen Shen¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3305-3319, 2025, DOI:10.32604/cmc.2025.065665 - 03 July 2025

Abstract Zero Trust Network (ZTN) enhances network security through strict authentication and access control. However, in the ZTN, optimizing flow control to improve the quality of service is still facing challenges. Software Defined Network (SDN) provides solutions through centralized control and dynamic resource allocation, but the existing scheduling methods based on Deep Reinforcement Learning (DRL) are insufficient in terms of convergence speed and dynamic optimization capability. To solve these problems, this paper proposes DRL-AMIR, which is an efficient flow scheduling method for software defined ZTN. This method constructs a flow scheduling optimization model that comprehensively considers… More >

Hui Xu, Wei Huang^*, Longtan Bai

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3053-3073, 2025, DOI:10.32604/cmc.2025.064930 - 03 July 2025

Abstract With the birth of Software-Defined Networking (SDN), integration of both SDN and traditional architectures becomes the development trend of computer networks. Network intrusion detection faces challenges in dealing with complex attacks in SDN environments, thus to address the network security issues from the viewpoint of Artificial Intelligence (AI), this paper introduces the Crayfish Optimization Algorithm (COA) to the field of intrusion detection for both SDN and traditional network architectures, and based on the characteristics of the original COA, an Improved Crayfish Optimization Algorithm (ICOA) is proposed by integrating strategies of elite reverse learning, Levy flight,… More >

Lin Luo^1,2,*, Junfeng Yuan¹, Yanling Wang¹, Rui Zhu¹, Huilin Xu¹, Siyuan Bi¹, Zhongge Zhang¹

International Journal of Mental Health Promotion, Vol.27, No.6, pp. 863-876, 2025, DOI:10.32604/ijmhp.2025.064385 - 30 June 2025

Abstract Objectives: Social Network Addiction (SNA) is becoming increasingly prevalent among college students; however, there remains a lack of consensus regarding the measurement tools and their optimal cutoff score. This study aims to validate the 21-item Social Network Addiction Scale-Chinese (SNAS-C) in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population. Methods: A cross-sectional survey was conducted, recruiting 3387 college students. Latent profile analysis (LPA) and receiver operating characteristic (ROC) curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C. Results:… More >

Sangmin Kim¹, Byeongcheon Lee¹, Muazzam Maqsood², Jihoon Moon^3,*, Seungmin Rho^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2079-2108, 2025, DOI:10.32604/cmes.2025.061653 - 30 May 2025

Abstract The increased accessibility of social networking services (SNSs) has facilitated communication and information sharing among users. However, it has also heightened concerns about digital safety, particularly for children and adolescents who are increasingly exposed to online grooming crimes. Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims. However, research on grooming detection in South Korea remains limited, as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations, leading to inaccurate classifications. To address these issues, this study proposes a novel… More >

Abdullah M. Alnajim^1,*, Faisal Mohammed Alotaibi^2,#, Sheroz Khan^3,#

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4515-4535, 2025, DOI:10.32604/cmc.2025.063139 - 19 May 2025

Abstract Distributed denial of service (DDoS) attacks are common network attacks that primarily target Internet of Things (IoT) devices. They are critical for emerging wireless services, especially for applications with limited latency. DDoS attacks pose significant risks to entrepreneurial businesses, preventing legitimate customers from accessing their websites. These attacks require intelligent analytics before processing service requests. Distributed denial of service (DDoS) attacks exploit vulnerabilities in IoT devices by launching multi-point distributed attacks. These attacks generate massive traffic that overwhelms the victim’s network, disrupting normal operations. The consequences of distributed denial of service (DDoS) attacks are typically… More >