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  • Open Access

    ARTICLE

    Designing a Secure and Scalable Data Sharing Mechanism Using Decentralized Identifiers (DID)

    Iuon-Chang Lin1, I-Ling Yeh1, Ching-Chun Chang2, Jui-Chuan Liu3, Chin-Chen Chang3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 809-822, 2024, DOI:10.32604/cmes.2024.051612 - 20 August 2024

    Abstract Centralized storage and identity identification methods pose many risks, including hacker attacks, data misuse, and single points of failure. Additionally, existing centralized identity management methods face interoperability issues and rely on a single identity provider, leaving users without control over their identities. Therefore, this paper proposes a mechanism for identity identification and data sharing based on decentralized identifiers. The scheme utilizes blockchain technology to store the identifiers and data hashed on the chain to ensure permanent identity recognition and data integrity. Data is stored on InterPlanetary File System (IPFS) to avoid the risk of single More >

  • Open Access

    ARTICLE

    Anomaly Detection in Imbalanced Encrypted Traffic with Few Packet Metadata-Based Feature Extraction

    Min-Gyu Kim1, Hwankuk Kim2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 585-607, 2024, DOI:10.32604/cmes.2024.051221 - 20 August 2024

    Abstract In the IoT (Internet of Things) domain, the increased use of encryption protocols such as SSL/TLS, VPN (Virtual Private Network), and Tor has led to a rise in attacks leveraging encrypted traffic. While research on anomaly detection using AI (Artificial Intelligence) is actively progressing, the encrypted nature of the data poses challenges for labeling, resulting in data imbalance and biased feature extraction toward specific nodes. This study proposes a reconstruction error-based anomaly detection method using an autoencoder (AE) that utilizes packet metadata excluding specific node information. The proposed method omits biased packet metadata such as… More >

  • Open Access

    ARTICLE

    A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

    Borja Bordel Sánchez1,*, Ramón Alcarria2, Tomás Robles1

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 631-654, 2024, DOI:10.32604/cmes.2024.050349 - 20 August 2024

    Abstract Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into… More >

  • Open Access

    ARTICLE

    PARE: Privacy-Preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

    Peicong He, Yang Xin*, Yixian Yang

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3067-3084, 2024, DOI:10.32604/cmc.2024.054777 - 15 August 2024

    Abstract The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain More >

  • Open Access

    ARTICLE

    Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

    Uddagiri Sirisha1,, Parvathaneni Naga Srinivasu2,3,*, Panguluri Padmavathi4, Seongki Kim5,, Aruna Pavate6, Jana Shafi7, Muhammad Fazal Ijaz8,*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2301-2330, 2024, DOI:10.32604/cmc.2024.053132 - 15 August 2024

    Abstract Fetal health care is vital in ensuring the health of pregnant women and the fetus. Regular check-ups need to be taken by the mother to determine the status of the fetus’ growth and identify any potential problems. To know the status of the fetus, doctors monitor blood reports, Ultrasounds, cardiotocography (CTG) data, etc. Still, in this research, we have considered CTG data, which provides information on heart rate and uterine contractions during pregnancy. Several researchers have proposed various methods for classifying the status of fetus growth. Manual processing of CTG data is time-consuming and unreliable.… More >

  • Open Access

    ARTICLE

    Sec-Auditor: A Blockchain-Based Data Auditing Solution for Ensuring Integrity and Semantic Correctness

    Guodong Han, Hecheng Li*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2121-2137, 2024, DOI:10.32604/cmc.2024.053077 - 15 August 2024

    Abstract Currently, there is a growing trend among users to store their data in the cloud. However, the cloud is vulnerable to persistent data corruption risks arising from equipment failures and hacker attacks. Additionally, when users perform file operations, the semantic integrity of the data can be compromised. Ensuring both data integrity and semantic correctness has become a critical issue that requires attention. We introduce a pioneering solution called Sec-Auditor, the first of its kind with the ability to verify data integrity and semantic correctness simultaneously, while maintaining a constant communication cost independent of the audited… More >

  • Open Access

    ARTICLE

    Blockchain-Enabled Federated Learning for Privacy-Preserving Non-IID Data Sharing in Industrial Internet

    Qiuyan Wang, Haibing Dong*, Yongfei Huang, Zenglei Liu, Yundong Gou

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1967-1983, 2024, DOI:10.32604/cmc.2024.052775 - 15 August 2024

    Abstract Sharing data while protecting privacy in the industrial Internet is a significant challenge. Traditional machine learning methods require a combination of all data for training; however, this approach can be limited by data availability and privacy concerns. Federated learning (FL) has gained considerable attention because it allows for decentralized training on multiple local datasets. However, the training data collected by data providers are often non-independent and identically distributed (non-IID), resulting in poor FL performance. This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain… More >

  • Open Access

    ARTICLE

    Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation

    Ruijin Xue1,2, Shuang Feng1,2,*, Qi Wang1,2

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3107-3122, 2024, DOI:10.32604/cmc.2024.052600 - 15 August 2024

    Abstract Users’ interests are often diverse and multi-grained, with their underlying intents even more so. Effectively capturing users’ interests and uncovering the relationships between diverse interests are key to news recommendation. Meanwhile, diversity is an important metric for evaluating news recommendation algorithms, as users tend to reject excessive homogeneous information in their recommendation lists. However, recommendation models themselves lack diversity awareness, making it challenging to achieve a good balance between the accuracy and diversity of news recommendations. In this paper, we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity. Unlike… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

    Fang Hu1, Siyi Qiu2, Xiaolian Yang1, Chaolei Wu1, Miguel Baptista Nunes3, Hui Chen4,*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2897-2915, 2024, DOI:10.32604/cmc.2024.052570 - 15 August 2024

    Abstract As the volume of healthcare and medical data increases from diverse sources, real-world scenarios involving data sharing and collaboration have certain challenges, including the risk of privacy leakage, difficulty in data fusion, low reliability of data storage, low effectiveness of data sharing, etc. To guarantee the service quality of data collaboration, this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning, termed FL-HMChain. This system is composed of three layers: Data extraction and storage, data management, and data application. Focusing on healthcare and medical data, a healthcare and… More >

  • Open Access

    ARTICLE

    Dynamic Forecasting of Traffic Event Duration in Istanbul: A Classification Approach with Real-Time Data Integration

    Mesut Ulu1,*, Yusuf Sait Türkan2, Kenan Mengüç3, Ersin Namlı2, Tarık Küçükdeniz2

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2259-2281, 2024, DOI:10.32604/cmc.2024.052323 - 15 August 2024

    Abstract Today, urban traffic, growing populations, and dense transportation networks are contributing to an increase in traffic incidents. These incidents include traffic accidents, vehicle breakdowns, fires, and traffic disputes, resulting in long waiting times, high carbon emissions, and other undesirable situations. It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities. This study presents a model for forecasting the traffic incident duration of traffic events with high precision. The proposed model goes through a 4-stage process using various features to predict the… More >

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