Forthcoming Articles

International Journal of High Performance Systems Architecture

International Journal of High Performance Systems Architecture (IJHPSA)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are also listed here. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of High Performance Systems Architecture (One paper in press)

Regular Issues

  • Evaluation of Supervised Machine Learning Methods in Detection of Phishing Threats   Order a copy of this article
    by Ivan Cviti?, Dragan Perakovi? 
    Abstract: This paper describes the development of seven machine learning models using the publicly available ISCX-URL2016 dataset. The models were designed to per-form multiple classifications, and their performance was evaluated using J48, Random Forest, Random Tree, Lazy IBk, BayesNet, and Naive Bayes algorithms. The dataset underwent preprocessing, resulting in 77 attributes, and the number of attributes for each model was determined using the InfoGain method. The results indicate that malicious website URLs can be classified into five predefined classes based on their features with high accuracy. The Random Forest, J48, Random Tree, and Lazy IBk algorithms achieved the highest accuracy rates, ranging from 94.52% to 98.43%. The Random Forest algorithm was further evaluated using machine learning metrics such as sensitivity, specificity, precision, recall, and f-measure, which demonstrated its effectiveness.
    Keywords: Cybersecurity; Data preprocessing; Decision tree; Malware detection; Feature se-lection.
    DOI: 10.1504/IJHPSA.2025.10075046
     

 

Return to top