Authors:
Peter Wafik
1
;
Alessio Botta
2
;
Luigi Gallo
2
;
Gennaro Esposito Mocerino
2
;
Cornelia Herbert
3
;
Ivan Annicchiarico
3
;
Alia El Bolock
1
and
Slim Abdennadher
1
Affiliations:
1
Department of Informatics and Computer Science, German International University, Cairo, Egypt
;
2
Department of Electrical Engineering and Information Technologies, University of Napoli Federico II, Naples, Italy
;
3
Department of Applied Emotion and Motivation Psychology, Ulm University, Ulm, Germany
Keyword(s):
Clustering, Predictive Clustering, Deep Learning, Neural Networks, Behavioral Analysis, Personalized Content Delivery, Social Engineering.
Abstract:
This study introduces a predictive framework to address a gap in user profiling, integrating advanced clustering, dimensionality reduction, and deep learning techniques to analyze the relationship between user profiles and email phishing susceptibility. Using data from the Spamley platform (Gallo et al., 2024), the proposed framework combines deep clustering and predictive models, achieving a Silhouette Score of 0.83, a Davies-Bouldin Index of 0.42, and a Calinski-Harabasz Index of 1676.2 with k-means and Self-Organizing Maps (SOM) for clustering user profiles. The results further highlight the effectiveness of Linear Support Vector Machines (SVM) and neural network models in classifying cluster membership, providing valuable decision-making insights. These findings demonstrate the efficacy of advanced non-linear methods for clustering complex user profile features, as well as the overall success of the semi-supervised model in enhancing clustering accuracy and predictive performance
. The framework lays a strong foundation for future research on tailored anti-phishing strategies and enhancing user resilience.
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