Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (62)
  • Open Access

    ARTICLE

    Several Attacks on Attribute-Based Encryption Schemes

    Phi Thuong Le1, Huy Quoc Le2, Viet Cuong Trinh1,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4741-4756, 2025, DOI:10.32604/cmc.2025.064486 - 19 May 2025

    Abstract Attribute-based encryption () is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes. is widely applied in cloud storage, file sharing, e-Health, and digital rights management. schemes rely on hard cryptographic assumptions such as pairings and others (pairing-free) to ensure their security against external and internal attacks. Internal attacks are carried out by authorized users who misuse their access to compromise security with potentially malicious intent. One common internal attack is the attribute collusion attack, in which users with different attribute keys collaborate to decrypt data they could not… More >

  • Open Access

    ARTICLE

    BIG-ABAC: Leveraging Big Data for Adaptive, Scalable, and Context-Aware Access Control

    Sondes Baccouri1,2,#,*, Takoua Abdellatif 3,#

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1071-1093, 2025, DOI:10.32604/cmes.2025.062902 - 11 April 2025

    Abstract Managing sensitive data in dynamic and high-stakes environments, such as healthcare, requires access control frameworks that offer real-time adaptability, scalability, and regulatory compliance. BIG-ABAC introduces a transformative approach to Attribute-Based Access Control (ABAC) by integrating real-time policy evaluation and contextual adaptation. Unlike traditional ABAC systems that rely on static policies, BIG-ABAC dynamically updates policies in response to evolving rules and real-time contextual attributes, ensuring precise and efficient access control. Leveraging decision trees evaluated in real-time, BIG-ABAC overcomes the limitations of conventional access control models, enabling seamless adaptation to complex, high-demand scenarios. The framework adheres to the… More >

  • Open Access

    ARTICLE

    A Verifiable Trust-Based CP-ABE Access Control Scheme for Cloud-Assisted Renewable Energy Systems

    Jiyu Zhang1,*, Kehe Wu1, Ruomeng Yan1, Zheng Tian2, Yizhen Sun2, Yuxi Wu2, Yaogong Guo3

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1211-1232, 2025, DOI:10.32604/cmc.2024.055243 - 03 January 2025

    Abstract Renewable Energy Systems (RES) provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers (ES). However, securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research topic. Additionally, managing attributes, including addition, deletion, and modification, is a crucial issue in the access control scheme for RES. To address these security concerns, a trust-based ciphertext-policy attribute-based encryption (CP-ABE) device access control scheme is proposed for RES (TB-CP-ABE). This… More >

  • Open Access

    ARTICLE

    An Asynchronous Data Transmission Policy for Task Offloading in Edge-Computing Enabled Ultra-Dense IoT

    Dayong Wang1,*, Kamalrulnizam Bin Abu Bakar1, Babangida Isyaku2, Liping Lei3

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4465-4483, 2024, DOI:10.32604/cmc.2024.059616 - 19 December 2024

    Abstract In recent years, task offloading and its scheduling optimization have emerged as widely discussed and significant topics. The multi-objective optimization problems inherent in this domain, particularly those related to resource allocation, have been extensively investigated. However, existing studies predominantly focus on matching suitable computational resources for task offloading requests, often overlooking the optimization of the task data transmission process. This inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network, resulting in increased service times due to elevated network transmission latencies and idle computational resources.… More >

  • Open Access

    ARTICLE

    Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

    Taha Yacine Trad1,*, Kheireddine Choutri1, Mohand Lagha1, Souham Meshoul2, Fouad Khenfri3, Raouf Fareh4, Hadil Shaiba5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4757-4786, 2024, DOI:10.32604/cmc.2024.055634 - 19 December 2024

    Abstract The popularity of quadrotor Unmanned Aerial Vehicles (UAVs) stems from their simple propulsion systems and structural design. However, their complex and nonlinear dynamic behavior presents a significant challenge for control, necessitating sophisticated algorithms to ensure stability and accuracy in flight. Various strategies have been explored by researchers and control engineers, with learning-based methods like reinforcement learning, deep learning, and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control systems. This paper investigates a Reinforcement Learning (RL) approach for both high and low-level quadrotor control systems, focusing on attitude stabilization and position… More >

  • Open Access

    ARTICLE

    Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

    Li Wang1,*, Xiaoyong Wang2

    Energy Engineering, Vol.121, No.12, pp. 3953-3979, 2024, DOI:10.32604/ee.2024.056705 - 22 November 2024

    Abstract Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and… More >

  • Open Access

    ARTICLE

    Two-Stage Client Selection Scheme for Blockchain-Enabled Federated Learning in IoT

    Xiaojun Jin1, Chao Ma2,*, Song Luo2, Pengyi Zeng1, Yifei Wei1

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2317-2336, 2024, DOI:10.32604/cmc.2024.055344 - 18 November 2024

    Abstract Federated learning enables data owners in the Internet of Things (IoT) to collaborate in training models without sharing private data, creating new business opportunities for building a data market. However, in practical operation, there are still some problems with federated learning applications. Blockchain has the characteristics of decentralization, distribution, and security. The blockchain-enabled federated learning further improve the security and performance of model training, while also expanding the application scope of federated learning. Blockchain has natural financial attributes that help establish a federated learning data market. However, the data of federated learning tasks may be… More >

  • Open Access

    ARTICLE

    Reversible Data Hiding Algorithm in Encrypted Images Based on Adaptive Median Edge Detection and Ciphertext-Policy Attribute-Based Encryption

    Zongbao Jiang, Minqing Zhang*, Weina Dong, Chao Jiang, Fuqiang Di

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1123-1155, 2024, DOI:10.32604/cmc.2024.055120 - 15 October 2024

    Abstract With the rapid advancement of cloud computing technology, reversible data hiding algorithms in encrypted images (RDH-EI) have developed into an important field of study concentrated on safeguarding privacy in distributed cloud environments. However, existing algorithms often suffer from low embedding capacities and are inadequate for complex data access scenarios. To address these challenges, this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection (AMED) and ciphertext-policy attribute-based encryption (CP-ABE). This proposed algorithm enhances the conventional median edge detection (MED) by incorporating dynamic variables to improve pixel prediction… More >

  • Open Access

    ARTICLE

    Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

    Aljuaid Turkea Ayedh M1,2,*, Ainuddin Wahid Abdul Wahab1,*, Mohd Yamani Idna Idris1,3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4663-4686, 2024, DOI:10.32604/cmc.2024.055287 - 12 September 2024

    Abstract Organizations are adopting the Bring Your Own Device (BYOD) concept to enhance productivity and reduce expenses. However, this trend introduces security challenges, such as unauthorized access. Traditional access control systems, such as Attribute-Based Access Control (ABAC) and Role-Based Access Control (RBAC), are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources. This paper proposes a method for enforcing access decisions that is adaptable and dynamic, based on multilayer hybrid deep learning techniques, particularly the Tabular Deep Neural Network TabularDNN method. This technique transforms… More >

  • Open Access

    ARTICLE

    Automatic Generation of Attribute-Based Access Control Policies from Natural Language Documents

    Fangfang Shan1,2,*, Zhenyu Wang1,2, Mengyao Liu1,2, Menghan Zhang1,2

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3881-3902, 2024, DOI:10.32604/cmc.2024.055167 - 12 September 2024

    Abstract In response to the challenges of generating Attribute-Based Access Control (ABAC) policies, this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents. This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model. The manual retrieval and analysis involved in this transition are inefficient, prone to errors, and costly. Most organizations have high-level specifications defined for security policies that include a set of access control policies, which often exist in the form of natural language documents. Utilizing… More >

Displaying 1-10 on page 1 of 62. Per Page