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p-Index From 2021 - 2026
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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Abualkibash, Munther
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Phishing Website Detection Using Several Machine Learning Algorithms: A Review Paper Veach, Alexander M.; Abualkibash, Munther
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 3 No. 2 (2022): International Journal of Informatics, Information System and Computer Engineeri
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v3i2.8805

Abstract

Phishing is one of the major web social engineering attacks. This has led to demand for a better way to predict and stop them in a commercial environment. This paper seeks to understand the research done in the field and analyse the next steps forward. This is done by focusing on what goes into the selection of proper features, from manual selection to the use of Genetic Algorithms such as ADABoost and MultiBoost. Then a look into the classifiers in use, Neural Networks and Ensemble algorithms which were prominent alongside some novel approaches. This information is then processed into a framework for cloud-based and client-based phishing website detection, alongside suggestions for possible future research and experiments that could help progress the field.
Testing Deep Learning Methods to Predict Ransowmare Activity from Hybrid Analysis Veach, Alexander M.; Abualkibash, Munther
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This article focuses on using deep learning methods to predict ransomware from hybrid analysis samples. Other similar research is analysed to understand the common methods of detection used to predict ransomware using various methods of analysis. By using this knowledge an experiment is created which tests the performance of a model created from hybrid analysis of ransomware samples. The training dataset used is made up of more than five hundred samples containing 38 different ransomware families and benign Windows program samples. The resultant model was then tested against a dataset include ransomware families not represented in the training dataset, which showed a decrease in performance. These results were then compared to other research’s reported results which highlights potential issues in the way artificial intelligence models are tested and reported. The paper then proposes a focus on more complex methods of prediction, and other potential methods to ensure the models created are externally as effective as they report.
Co-Authors Veach, Alexander M.