A Comprehensive Systematic Review of Neural Networks and Their Impact on the Detection of Malicious Websites in Network Users

Authors

  • Javier Gamboa-Cruzado Universidad Autónoma del Perú https://orcid.org/0000-0002-0461-4152
  • Juan Briceño-Ochoa Universidad Autónoma del Perú
  • Marco Huaysara-Ancco Universidad Autónoma del Perú
  • Alberto Alva Arévalo Universidad Nacional de San Martín
  • Caleb Ríos Vargas Universidad Nacional de San Martín
  • Magaly Arangüena Yllanes Universidad Nacional José María Arguedas
  • Liset S. Rodriguez-Baca Universidad Autónoma del Perú

DOI:

https://doi.org/10.3991/ijim.v17i01.36371

Keywords:

Machine Learning, Neural Network, Web site detection, malicious web sites, Algorithms, Systematic Literature Review

Abstract


The large branches of Machine Learning represent an immense support for the detection of malicious websites, they can predict whether a URL is malicious or benign, leaving aside the cyber attacks that can generate for network users who are unaware of them. The objective of the research was to know the state of the art about Neural Networks and their impact for the Detection of malicious Websites in network users. For this purpose, a systematic literature review (SLR) was conducted from 2017 to 2021. The search identified 561 963 papers from different sources such as Taylor & Francis Online, IEEE Xplore, ARDI, ScienceDirect, Wiley Online Library, ACM Digital Library and Microsoft Academic. Of the papers only 82 were considered based on exclusion criteria formulated by the author. As a result of the SLR, studies focused on machine learning (ML), where it recommends the use of algorithms to have a better and efficient prediction of malicious websites. For the researchers, this review presents a mapping of the findings on the most used machine learning techniques for malicious website detection, which are essential for a study because they increase the accuracy of an algorithm. It also shows the main machine learning methodologies that are used in the research papers.

Author Biographies

Javier Gamboa-Cruzado, Universidad Autónoma del Perú

Works at the Faculty of Systems Engineering of the Universidad Autónoma del Perú, Lima, Peru. He is Doctor in Systems Engineering and Doctor in Administrative Sciences. He has published several articles in international journals and conferences. His research interests are in machine learning, big data, the internet of things, natural language processing, and business intelligence.

Juan Briceño-Ochoa, Universidad Autónoma del Perú

graduate of the Faculty of Engineering and Architecture at the Universidad Autónoma del Perú, Peru. His research interests are in web systems, internet of things, and big data.

Marco Huaysara-Ancco, Universidad Autónoma del Perú

graduate at the Faculty of Engineering and Architecture at the Universidad Autónoma del Perú, with extensive experience in database management and development of mobile applications.

Alberto Alva Arévalo, Universidad Nacional de San Martín

Professor at the Faculty of Systems Engineering and Informatics of the Universidad Nacional de San Martín, Peru. He is a Systems Engineer, and has a Master's Degree in Sciences with a Mention in Information Technology. His research interests are related to Information Technology and Communications.

Caleb Ríos Vargas, Universidad Nacional de San Martín

is a University Professor, Works Consultant, is a Civil Engineer by profession, graduated from the Universidad Nacional de San Martín - Tarapoto and has a Master of Science with a mention in Transportation Engineering from the National University of Engineering of Lima - Peru, holds a Doctorate in Business Management from the National University of San Martín. His interests are related to research and technological innovation.

Magaly Arangüena Yllanes, Universidad Nacional José María Arguedas

is working at the Faculty of Engineering of the José María Arguedas National University, Apurímac, Peru. She has a Master's degree in Magister Scientiae in Computer Science, mention in Management of Information and Communications Technologies. She has published articles in national magazines. Her research interests are Data and Information Analytics and Visualization, Governance and Business Intelligence.

Liset S. Rodriguez-Baca, Universidad Autónoma del Perú

Systems Engineer, graduated in Education, Master in Systems Engineering with Mention in Management and Management in Information Technology, Master in Strategic Business Management, Doctor in Education Sciences. Director of the Professional School of Systems Engineering at the Universidad Autónoma del Perú,

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Published

2023-01-10

How to Cite

Gamboa-Cruzado, J., Briceño-Ochoa, J., Huaysara-Ancco, M., Alva Arévalo, A., Ríos Vargas, C., Arangüena Yllanes, M., & Rodriguez-Baca, L. S. (2023). A Comprehensive Systematic Review of Neural Networks and Their Impact on the Detection of Malicious Websites in Network Users. International Journal of Interactive Mobile Technologies (iJIM), 17(01), pp. 108–128. https://doi.org/10.3991/ijim.v17i01.36371

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Papers