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  • Open Access

    ARTICLE

    Cyber-Integrated Predictive Framework for Gynecological Cancer Detection: Leveraging Machine Learning on Numerical Data amidst Cyber-Physical Attack Resilience

    Muhammad Izhar1,*, Khadija Parwez2, Saman Iftikhar3, Adeel Ahmad4, Shaikhan Bawazeer3, Saima Abdullah4

    Journal on Artificial Intelligence, Vol.7, pp. 55-83, 2025, DOI:10.32604/jai.2025.062479 - 25 April 2025

    Abstract The growing intersection of gynecological cancer diagnosis and cybersecurity vulnerabilities in healthcare necessitates integrated solutions that address both diagnostic accuracy and data protection. With increasing reliance on IoT-enabled medical devices, digital twins, and interconnected healthcare systems, the risk of cyber-physical attacks has escalated significantly. Traditional approaches to machine learning (ML)–based diagnosis often lack real-time threat adaptability and privacy preservation, while cybersecurity frameworks fall short in maintaining clinical relevance. This study introduces HealthSecureNet, a novel Cyber-Integrated Predictive Framework designed to detect gynecological cancer and mitigate cybersecurity threats in real time simultaneously. The proposed model employs a… More >

  • Open Access

    ARTICLE

    Evaluation and Benchmarking of Cybersecurity DDoS Attacks Detection Models through the Integration of FWZIC and MABAC Methods

    Alaa Mahmood, İsa Avcı*

    Computer Systems Science and Engineering, Vol.49, pp. 401-417, 2025, DOI:10.32604/csse.2025.062413 - 25 April 2025

    Abstract A Distributed Denial-of-Service (DDoS) attack poses a significant challenge in the digital age, disrupting online services with operational and financial consequences. Detecting such attacks requires innovative and effective solutions. The primary challenge lies in selecting the best among several DDoS detection models. This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making (MCDM) techniques to compare and select the most effective models. The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion (FWZIC) and Multi-Attribute Boundary Approximation Area Comparison (MABAC) methodologies.… More >

  • Open Access

    REVIEW

    Blockchain Integration in IoT: Applications, Opportunities, and Challenges

    Mozhgan Gholami1, Ali Ghaffari1,2,3,*, Nahideh Derakhshanfard1, Nadir iBRAHIMOĞLU4, Ali Asghar Pourhaji Kazem2

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1561-1605, 2025, DOI:10.32604/cmc.2025.063304 - 16 April 2025

    Abstract The Internet has been enhanced recently by blockchain and Internet of Things (IoT) networks. The Internet of Things is a network of various sensor-equipped devices. It gradually integrates the Internet, sensors, and cloud computing. Blockchain is based on encryption algorithms, which are shared database technologies on the Internet. Blockchain technology has grown significantly because of its features, such as flexibility, support for integration, anonymity, decentralization, and independent control. Computational nodes in the blockchain network are used to verify online transactions. However, this integration creates scalability, interoperability, and security challenges. Over the last decade, several advancements… More >

  • Open Access

    ARTICLE

    A Novel Approach to Enhanced Cancelable Multi-Biometrics Personal Identification Based on Incremental Deep Learning

    Ali Batouche1, Souham Meshoul2,*, Hadil Shaiba3, Mohamed Batouche2,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1727-1752, 2025, DOI:10.32604/cmc.2025.063227 - 16 April 2025

    Abstract The field of biometric identification has seen significant advancements over the years, with research focusing on enhancing the accuracy and security of these systems. One of the key developments is the integration of deep learning techniques in biometric systems. However, despite these advancements, certain challenges persist. One of the most significant challenges is scalability over growing complexity. Traditional methods either require maintaining and securing a growing database, introducing serious security challenges, or relying on retraining the entire model when new data is introduced—a process that can be computationally expensive and complex. This challenge underscores the… More >

  • Open Access

    ARTICLE

    Machine Learning for Smart Soil Monitoring

    Khaoula Ben Abdellafou1, Kamel Zidi2, Ahamed Aljuhani1, Okba Taouali1,*, Mohamed Faouzi Harkat3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3007-3023, 2025, DOI:10.32604/cmc.2025.063146 - 16 April 2025

    Abstract Environmental protection requires identifying, investigating, and raising awareness about safeguarding nature from the harmful effects of both anthropogenic and natural events. This process of environmental protection is essential for maintaining human well-being. In this context, it is critical to monitor and safeguard the personal environment, which includes maintaining a healthy diet and ensuring plant safety. Living in a balanced environment and ensuring the safety of plants for green spaces and a healthy diet require controlling the nature and quality of the soil in our environment. To ensure soil quality, it is imperative to monitor and… More >

  • Open Access

    ARTICLE

    A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

    Federica Uccello1,2, Marek Pawlicki3,4, Salvatore D'Antonio1, Rafał Kozik3,4, Michał Choraś3,4,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1607-1621, 2025, DOI:10.32604/cmc.2025.062801 - 16 April 2025

    Abstract The growing sophistication of cyberthreats, among others the Distributed Denial of Service attacks, has exposed limitations in traditional rule-based Security Information and Event Management systems. While machine learning–based intrusion detection systems can capture complex network behaviours, their “black-box” nature often limits trust and actionable insight for security operators. This study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules, thereby enhancing the detection of Distributed Denial of Service (DDoS) attacks. The proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules More >

  • Open Access

    ARTICLE

    Joint Watermarking and Encryption for Social Image Sharing

    Conghuan Ye1,*, Shenglong Tan1, Shi Li1, Jun Wang1, Qiankun Zuo1, Bing Xiong2

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2927-2946, 2025, DOI:10.32604/cmc.2025.062051 - 16 April 2025

    Abstract With the fast development of multimedia social platforms, content dissemination on social media platforms is becoming more popular. Social image sharing can also raise privacy concerns. Image encryption can protect social images. However, most existing image protection methods cannot be applied to multimedia social platforms because of encryption in the spatial domain. In this work, the authors propose a secure social image-sharing method with watermarking/fingerprinting and encryption. First, the fingerprint code with a hierarchical community structure is designed based on social network analysis. Then, discrete wavelet transform (DWT) from block discrete cosine transform (DCT) directly… More >

  • Open Access

    ARTICLE

    A Lightweight Convolutional Neural Network with Squeeze and Excitation Module for Security Authentication Using Wireless Channel

    Xiaoying Qiu1,*, Xiaoyu Ma1, Guangxu Zhao1, Jinwei Yu2, Wenbao Jiang1, Zhaozhong Guo1, Maozhi Xu3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2025-2040, 2025, DOI:10.32604/cmc.2025.061869 - 16 April 2025

    Abstract Physical layer authentication (PLA) in the context of the Internet of Things (IoT) has gained significant attention. Compared with traditional encryption and blockchain technologies, PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself. Some existing PLA solutions rely on static mechanisms, which are insufficient to address the authentication challenges in fifth generation (5G) and beyond wireless networks. Additionally, with the massive increase in mobile device access, the communication security of the IoT is vulnerable to spoofing attacks. To overcome the above challenges, this paper proposes a lightweight deep More >

  • Open Access

    ARTICLE

    TIPS: Tailored Information Extraction in Public Security Using Domain-Enhanced Large Language Model

    Yue Liu1, Qinglang Guo2, Chunyao Yang1, Yong Liao1,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2555-2572, 2025, DOI:10.32604/cmc.2025.060318 - 16 April 2025

    Abstract Processing police incident data in public security involves complex natural language processing (NLP) tasks, including information extraction. This data contains extensive entity information—such as people, locations, and events—while also involving reasoning tasks like personnel classification, relationship judgment, and implicit inference. Moreover, utilizing models for extracting information from police incident data poses a significant challenge—data scarcity, which limits the effectiveness of traditional rule-based and machine-learning methods. To address these, we propose TIPS. In collaboration with public security experts, we used de-identified police incident data to create templates that enable large language models (LLMs) to populate data More >

  • Open Access

    ARTICLE

    Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder

    Saad Alahmari1,*, Abdulwhab Alkharashi2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 849-873, 2025, DOI:10.32604/cmes.2025.062549 - 11 April 2025

    Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >

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