Malicious website detection through deep learning algorithms
Visualitza/Obre
10.1007/978-3-030-95467-3_37
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/363001
Tipus de documentText en actes de congrés
Data publicació2022
EditorSpringer Nature
Condicions d'accésAccés obert
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Abstract
Traditional methods that detect malicious websites, such as blacklists, do not update frequently, and they cannot detect new attackers. A system capable of detecting malicious activity using Deep Learning (DL) has been proposed to address this need. Starting from a dataset that contains both malevolent and benign websites, classification is done by extracting, parsing, analysing, and preprocessing the data. Additionally, the study proposes a Feed-Forward Neural Network (FFNN) to classify each sample. We evaluate different combinations of neurons in the model and perform in-depth research of the best performing network. The results show up to 99.88% of detection of malicious websites and 2.61% of false hits in the testing phase (i.e. malicious websites classified as benign), and 1.026% in the validation phase.
CitacióGutiérrez, N. [et al.]. Malicious website detection through deep learning algorithms. A: International Conference on Machine Learning, Optimization, and Data Science. "Machine Learning, Optimization, and Data Science, 7th International Conference, LOD 2021: Grasmere, UK, October 4-8, 2021: revised selected papers, part I". Springer Nature, 2022, p. 512-526. ISBN 978-3-030-95467-3. DOI 10.1007/978-3-030-95467-3_37.
ISBN978-3-030-95467-3
Versió de l'editorhttps://link.springer.com/chapter/10.1007/978-3-030-95467-3_37
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LOD_2021_Camera-Ready_71+(2).pdf | 888,0Kb | Visualitza/Obre |