Authors:
Claudia-Ioana Coste
;
Anca-Mirela Andreica
and
Camelia Chira
Affiliation:
Department of Computer Science, Faculty of Mathematics and Computer Science, Babes, -Bolyai University, Mihail Kogalniceanu Street, no. 1, Cluj-Napoca, Romania
Keyword(s):
Malicious Web Links Detection, Machine Learning Algorithms, Ensemble Models, Web-Malware.
Abstract:
Malicious links are becoming the main propagating vector for web-malware. They may lead to serious security issues, such as phishing, distribution of fake news and low-quality content, drive-by-downloads, and malicious code running. Malware link detection is a challenging domain because of the dynamics of the online environment, where web links and web content are always changing. Moreover, the detection should be fast and accurate enough that it will contribute to a better online experience. The present paper proposes to drive an experimental analysis on machine learning algorithms used in malicious web links detection. The algorithms chosen for analysis are Logistic Regression, Naı̈ve Bayes, Ada Boost, Gradient Boosted Tree, Linear Discriminant Analysis, Multi-layer Perceptron and Support Vector Machine with different kernel types. Our purpose is twofold. First, we compare these single algorithms run individually and calibrate their parameters. Secondly, we chose 10 models and used
them in ensemble models. The results of these experiments show that the ensemble models reach higher metric scores than the individual models, improving the maliciousness prediction up to 96% precision.
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