Using Ensemble Models for Malicious Web Links Detection

Claudia-Ioana Coste

2024

Abstract

Web technology advances faster than humans can adapt to it and develop the proper online skills. Most users are not experienced enough to have a good online knowledge on how to protect their data. Thus, many people can become vulnerable to threats. The most common online attacks are through malicious web links, which can deceive users into clicking them and running malicious code. The present approach proposed to advance the field of malicious web links detection through ensemble models by developing a nature-inspired ensemble. Our methodology is tested against two datasets, and we conduct an additional calibration step for all the models. For the first database, we managed to improve the detection accuracy from other solutions, by achieving 97.05%. In the case of the second dataset, our empirical strategy is not accurate enough, reaching just 91.12% accuracy. The proposed ensemble is heterogeneous, having a weight voting mechanism, where weights are generated with the Particle Swarm Optimization algorithm. To build the ensemble we compared 12 individual machine learning models, including Logistic Regression, Support Vector Machine, Adaptive Boosting, Random Forest, Decision Tree, K-Nearest Neighbor, Perceptron, Nearest Centroid, Passive Aggressive Classifier, Stochastic Gradient Descent, KMeans, and different variants for Naive Bayes.

Paper Citation

in Harvard Style

Coste C. (2024). Using Ensemble Models for Malicious Web Links Detection. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 657-664. DOI: 10.5220/0012381800003636

in Bibtex Style

@conference{icaart24,
author={Claudia-Ioana Coste},
title={Using Ensemble Models for Malicious Web Links Detection},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={657-664},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012381800003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Using Ensemble Models for Malicious Web Links Detection
SN - 978-989-758-680-4
AU - Coste C.
PY - 2024
SP - 657
EP - 664
DO - 10.5220/0012381800003636
PB - SciTePress