JCSOpen Access

Journal of Cyber Security

ISSN:2579-0072(print)
ISSN:2579-0064(online)
Publication Frequency:Continuously

  • Online
    Articles

    71

  • on board
    editors

    20

Special lssues

About the Journal

Cybersecurity is the basis of information dissemination in the internet age.The Journal of Cyber Security focuses on all aspects of sciences, technologies, and applications relating to hardware security, software security and system security.

Indexing and Abstracting



Starting from July 2023, Journal of Cyber Security will transition to a continuous publication model, accepted articles will be promptly published online upon completion of the peer review and production processes.

  • Open Access

    ARTICLE

    Comparative Analysis of Machine Learning Models for PDF Malware Detection: Evaluating Different Training and Testing Criteria

    Journal of Cyber Security, Vol.5, pp. 1-11, 2023, DOI:10.32604/jcs.2023.042501
    Abstract The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks. Portable Document Format (PDF) files have emerged as a major attack vector for malware due to their adaptability and wide usage. Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts, exploits, and malicious URLs. This paper presents a comparative analysis of machine learning (ML) techniques, including Naive Bayes (NB), K-Nearest Neighbor (KNN), Average One Dependency Estimator (A1DE), Random Forest (RF), and Support Vector Machine (SVM) for PDF malware detection. The study… More >

  • Open Access

    ARTICLE

    Detecting Phishing Using a Multi-Layered Social Engineering Framework

    Journal of Cyber Security, Vol.5, pp. 13-32, 2023, DOI:10.32604/jcs.2023.043359
    Abstract As businesses develop and expand with a significant volume of data, data protection and privacy become increasingly important. Research has shown a tremendous increase in phishing activities during and after COVID-19. This research aimed to improve the existing approaches to detecting phishing activities on the internet. We designed a multi-layered phish detection algorithm to detect and prevent phishing applications on the internet using URLs. In the algorithm, we considered technical dimensions of phishing attack prevention and mitigation on the internet. In our approach, we merge, Phishtank, Blacklist, Blocklist, and Whitelist to form our framework. A web application system and browser… More >

  • Open Access

    ARTICLE

    Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique

    Journal of Cyber Security, Vol.5, pp. 33-46, 2023, DOI:10.32604/jcs.2023.045422
    Abstract The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud, particularly in credit card transactions. Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising from potentially fraudulent activities. However, a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations. While sampling techniques can significantly reduce computational time, the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed. Such… More >

  • Open Access

    ARTICLE

    Empirical Analysis of Neural Networks-Based Models for Phishing Website Classification Using Diverse Datasets

    Journal of Cyber Security, Vol.5, pp. 47-66, 2023, DOI:10.32604/jcs.2023.045579
    Abstract Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information, a problem that persists despite user awareness. This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning (ML) models—Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories. Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%. On the other hand, LSTM shows the lowest accuracy of 96%. These findings underscore the potential of ML techniques in… More >

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