COVID-19 malicious domain names classification

Mvula, Paul K.; Branco, Paula; Jourdan, Guy-Vincent; Viktor, Herna L.

doi:10.1016/j.eswa.2022.117553

Cited by 10 publications

(1 citation statement)

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“…In [7], the main contribution of the paper is based on proposing a novel approach using character-level URL encoding to prevent phishing. In [32], the main contribution of the paper is based on proposing machine learning models that use a limited number of features to classify COVID-19 related domain names as malicious or legitimate for the purpose of improving anti-cyberattack or malware alternatives. In [12], the authors conducted a systematic literature research (SLR) to identify, evaluate and synthesize the results on Deep Learning approaches for phishing detection as reported by selected scientific publications.…”

Section: Related Workmentioning

confidence: 99%

Intelligent System for Phishing detection on web pages using Random Forest

Araujo Arévalo,

Felix-Diaz monzon,

Mansilla López

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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During the last years there have been many technological problems, among them, one of those that stands out for its impact worldwide is Phishing, which is a Social Engineering technique that seeks as a benefit to illegally appropriate confidential and private information. In Peru, this problem is spreading considerably due to the lack of interest of both state and private entities in being able to make a contingency plan against this problem, and more and more citizens are affected and there is no clear statement or response from the authorities. In response to this major problem, this paper proposes the use of an intelligent system that uses a Random Forest algorithm, which is based on machine learning and can detect in time the URLs that may be malicious.

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Section: Related Workmentioning

confidence: 99%