Chuang-Chieh Lin¹, Yung-Shen Huang², Shih-Yeh Chen^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.072890 - 09 December 2025

Abstract With the rapid development of generative artificial intelligence (GenAI), the task of story visualization, which transforms natural language narratives into coherent and consistent image sequences, has attracted growing research attention. However, existing methods still face limitations in balancing multi-frame character consistency and generation efficiency, which restricts their feasibility for large-scale practical applications. To address this issue, this study proposes a modular cloud-based distributed system built on Stable Diffusion. By separating the character generation and story generation processes, and integrating multi-feature control techniques, a caching mechanism, and an asynchronous task queue architecture, the system enhances generation… More >

Jay Barach^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.071705 - 09 December 2025

Abstract Human Resource (HR) operations increasingly rely on cloud-based platforms that provide hiring, payroll, employee management, and compliance services. These systems, typically built on multi-tenant microservice architectures, offer scalability and efficiency but also expand the attack surface for adversaries. Ransomware has emerged as a leading threat in this domain, capable of halting workflows and exposing sensitive employee records. Traditional defenses such as static hardening and signature-based detection often fail to address the dynamic requirements of HR Software as a Service (SaaS), where continuous availability and privacy compliance are critical. This paper presents a Moving Target Defense… More >

Haibo Lei^1,2, Xu An Wang^1,*, Wenhao Liu¹, Lingling Wu¹, Chao Zhang¹, Weiwei Jiang³, Xiao Zou⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.070030 - 09 December 2025

Abstract With the rapid expansion of the Internet of Things (IoT), user data has experienced exponential growth, leading to increasing concerns about the security and integrity of data stored in the cloud. Traditional schemes relying on untrusted third-party auditors suffer from both security and efficiency issues, while existing decentralized blockchain-based auditing solutions still face shortcomings in correctness and security. This paper proposes an improved blockchain-based cloud auditing scheme, with the following core contributions: Identifying critical logical contradictions in the original scheme, thereby establishing the foundation for the correctness of cloud auditing; Designing an enhanced mechanism that… More >

Yuchao Hou^1,2, Biaobiao Bai³, Shuai Zhao³, Yue Wang³, Jie Wang³, Zijian Li^4,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-30, 2026, DOI:10.32604/cmc.2025.069789 - 09 December 2025

Abstract Recently, large-scale deep learning models have been increasingly adopted for point cloud classification. However, these methods typically require collecting extensive datasets from multiple clients, which may lead to privacy leaks. Federated learning provides an effective solution to data leakage by eliminating the need for data transmission, relying instead on the exchange of model parameters. However, the uneven distribution of client data can still affect the model’s ability to generalize effectively. To address these challenges, we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework (FDASS-MRFCF).… More >

He Duan, Shi Zhang^*, Dayu Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069628 - 09 December 2025

Abstract Internet of Things (IoT) interconnects devices via network protocols to enable intelligent sensing and control. Resource-constrained IoT devices rely on cloud servers for data storage and processing. However, this cloud-assisted architecture faces two critical challenges: the untrusted cloud services and the separation of data ownership from control. Although Attribute-based Searchable Encryption (ABSE) provides fine-grained access control and keyword search over encrypted data, existing schemes lack of error tolerance in exact multi-keyword matching. In this paper, we proposed an attribute-based multi-keyword fuzzy searchable encryption with forward ciphertext search (FCS-ABMSE) scheme that avoids computationally expensive bilinear pairing… More >

Junjun Ren¹, Guoqiang Chen², Zheng-Yi Chai³, Dong Yuan^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068795 - 10 November 2025

Abstract Vehicle Edge Computing (VEC) and Cloud Computing (CC) significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit (RSU), thereby achieving lower delay and energy consumption. However, due to the limited storage capacity and energy budget of RSUs, it is challenging to meet the demands of the highly dynamic Internet of Vehicles (IoV) environment. Therefore, determining reasonable service caching and computation offloading strategies is crucial. To address this, this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading. By… More >

Daskshnamoorthy Manivannan^*

Journal on Internet of Things, Vol.7, pp. 71-97, 2025, DOI:10.32604/jiot.2025.072809 - 24 December 2025

Abstract Attribute-Based Encryption (ABE) secures data by tying decryption rights to user attributes instead of identities, enabling fine-grained access control. However, many ABE schemes are unsuitable for Internet of Things (IoT) due to limited device resources. This paper critically surveys ABE schemes developed specifically for IoT over the past decade, examining their evolution, strengths, limitations, and access control capabilities. It provides insights into their security, effectiveness, and real-world applicability, highlights the current state of ABE in securing IoT data and access, and discusses remaining challenges and open issues. More >

Dakshnamoorthy Manivannan^*

Computer Systems Science and Engineering, Vol.49, pp. 533-555, 2025, DOI:10.32604/csse.2025.072267 - 08 December 2025

Abstract Attribute-based Encryption (ABE) enhances the confidentiality of Electronic Health Records (EHR) (also known as Personal Health Records (PHR)) by binding access rights not to individual identities, but to user attribute sets such as roles, specialties, or certifications. This data-centric cryptographic paradigm enables highly fine-grained, policy-driven access control, minimizing the need for identity management and supporting scalable multi-user scenarios. This paper presents a comprehensive and critical survey of ABE schemes developed specifically for EHR/PHR systems over the past decade. It explores the evolution of these schemes, analyzing their design principles, strengths, limitations, and the level of More >

Daskshnamoorthy Manivannan^*

Journal of Cyber Security, Vol.7, pp. 505-531, 2025, DOI:10.32604/jcs.2025.072810 - 28 November 2025

Abstract Attribute-Based Encryption (ABE) secures data by linking decryption rights to user attributes rather than user identities, enabling fine-grained access control. While ABE is effective for enforcing access policies, integrating it with Searchable Encryption (SE)—which allows searching encrypted data without decryption—remains a complex challenge. This paper presents a comprehensive survey of ABE schemes that support SE proposed over the past decade. It critically analyzes their strengths, limitations, and access control capabilities. The survey offers insights into the security, efficiency, and practical applicability of these schemes, outlines the current landscape of ABE-integrated SE, and identifies key challenges More >

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