Reshma Siyal¹, Muhammad Asim^2,*, Long Jun¹, Mohammed Elaffendi², Sundas Iftikhar³, Rana Alnashwan⁴, Samia Allaoua Chelloug^4,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2637-2664, 2025, DOI:10.32604/cmc.2025.058717 - 17 February 2025

Abstract A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. More >

Shaozheng Zhang, Qiuyu Zhang^*, Jiahui Tang, Ruihua Xu

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2137-2158, 2025, DOI:10.32604/cmc.2024.057269 - 17 February 2025

Abstract Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a… More >

Shafi Ullah^1,*, Haidawati Muhammad Nasir², Kushsairy Kadir^3,*, Akbar Khan¹, Ahsanullah Memon⁴, Shanila Azhar¹, Ilyas Khan⁵, Muhammad Ashraf¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3223-3249, 2025, DOI:10.32604/cmc.2024.054676 - 17 February 2025

Abstract Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519; and (ii): elliptic curve More >

Muhammad Armghan Latif¹, Zohaib Mushtaq^2,*, Saifur Rahman³, Saad Arif⁴, Salim Nasar Faraj Mursal³, Muhammad Irfan³, Haris Aziz⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1667-1695, 2025, DOI:10.32604/cmes.2024.056850 - 27 January 2025

Abstract Ransomware attacks pose a significant threat to critical infrastructures, demanding robust detection mechanisms. This study introduces a hybrid model that combines vision transformer (ViT) and one-dimensional convolutional neural network (1DCNN) architectures to enhance ransomware detection capabilities. Addressing common challenges in ransomware detection, particularly dataset class imbalance, the synthetic minority oversampling technique (SMOTE) is employed to generate synthetic samples for minority class, thereby improving detection accuracy. The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features, resulting in comprehensive ransomware classification. Tested on the UNSW-NB15 More >

Haider H. Al-Mahmood^1,*, Saad N. Alsaad²

Intelligent Automation & Soft Computing, Vol.40, pp. 53-74, 2025, DOI:10.32604/iasc.2024.059691 - 10 January 2025

Abstract In secure communications, lightweight encryption has become crucial, particularly for resource-constrained applications such as embedded devices, wireless sensor networks, and the Internet of Things (IoT). As these systems proliferate, cryptographic approaches that provide robust security while minimizing computing overhead, energy consumption, and memory usage are becoming increasingly essential. This study examines lightweight encryption techniques utilizing chaotic maps to ensure secure data transmission. Two algorithms are proposed, both employing the Logistic map; the first approach utilizes two logistic chaotic maps, while the second algorithm employs a single logistic chaotic map. Algorithm 1, including a two-stage mechanism… More >

Jiyu Zhang^1,*, 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 >

Chanhyeong Cho¹, Byeori Kim², Haehyun Cho², Taek-Young Youn^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 813-834, 2025, DOI:10.32604/cmes.2024.056952 - 17 December 2024

Abstract With the rise of remote collaboration, the demand for advanced storage and collaboration tools has rapidly increased. However, traditional collaboration tools primarily rely on access control, leaving data stored on cloud servers vulnerable due to insufficient encryption. This paper introduces a novel mechanism that encrypts data in ‘bundle’ units, designed to meet the dual requirements of efficiency and security for frequently updated collaborative data. Each bundle includes updated information, allowing only the updated portions to be re-encrypted when changes occur. The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes, such… More >

Yuanhang Yang¹, Changjin Huang^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.010991

Abstract Achieving programmable and reversible deformations of soft materials is a long-standing goal for various applications in soft robotics, flexible electronics and many other fields. Swelling-induced shape-morphing has been intensively studied as one of the potential mechanisms. However, achieving an extremely large swelling ratio (>1000% in volume) remains challenging with existing swellable soft materials (e.g., hydrogels and water-swellable rubbers). Inspired by the shape change enabled by the osmosis-driven swelling in living organisms, herein, we report a polymer composite system composed of fine sodium chloride (NaCl) particles embedded in Ecoflex00-10 polymer. This Ecoflex00-10/NaCl polymer composite can achieve… More >

Xiaojun Jin¹, Chao Ma^2,*, 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 >

Chan Hyeong Cho, Hyun Min Song^*, Taek-Young Youn^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1911-1931, 2024, DOI:10.32604/cmes.2024.053430 - 31 October 2024

Abstract With the advancement of video recording devices and network infrastructure, we use surveillance cameras to protect our valuable assets. This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security. The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest (ROIs) within the video, linking these ROIs to generate unique IDs. These IDs are then combined with a master key to create entity-specific keys, which are used to encrypt the ROIs within the video. This system supports selective decryption, effectively protecting personal information More >