Abstract
This contribution proposes an ensemble classification model which is based on neural networks prediction models and well-known online incremental learning models.The considered neural network models belong to different families, namely long-short term memory, deep feed forward and convolutional neural networks. The incremental learning models considered are Passive Aggressive, Bernoulli Naive Bayes and Stochastic Gradient Descent Classifiers. This paper aims to develop a prediction model that reduces false positives (FP) while maintaining overall model performance. Moreover, the stability of the model over time and its ability to correctly classify phishing links, even if the concept shift occur, are under considerations. The ensemble model shows promising results, demonstrating its superiority over base models. Some proposed models significantly outperform some base models according to statistical tests.
Paper Type
Poster
DOI
10.62036/ISD.2025.70
Neural networks based ensemble classifier for phishing link detection
This contribution proposes an ensemble classification model which is based on neural networks prediction models and well-known online incremental learning models.The considered neural network models belong to different families, namely long-short term memory, deep feed forward and convolutional neural networks. The incremental learning models considered are Passive Aggressive, Bernoulli Naive Bayes and Stochastic Gradient Descent Classifiers. This paper aims to develop a prediction model that reduces false positives (FP) while maintaining overall model performance. Moreover, the stability of the model over time and its ability to correctly classify phishing links, even if the concept shift occur, are under considerations. The ensemble model shows promising results, demonstrating its superiority over base models. Some proposed models significantly outperform some base models according to statistical tests.
Recommended Citation
Gałka, W., Mrukowicz, M., Bentkowska, U. & Bazan, J.G. (2025). Neural networks based ensemble classifier for phishing link detectionIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.70