A Comprehensive Survey on Identification and Analysis of Phishing Website based on Machine Learning Methods
| Autor: | Mohammed Hazim Alkawaz, Stephanie Joanne Steven, Asif Iqbal Hajamydeen, Rusyaizila Ramli |
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| Rok vydání: | 2021 |
| Předmět: |
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Computer science business.industry Feature extraction computer.file_format Machine learning computer.software_genre Phishing Set (abstract data type) Cybercrime ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS Identification (information) Feature (machine learning) ComputingMilieux_COMPUTERSANDSOCIETY Artificial intelligence RDF business computer |
| Zdroj: | 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). |
| Popis: | Phishing is a cybercrime which is carried out by imitating a legal website to trick users to steal their personal data, including usernames, passwords, account numbers, national insurance numbers, etc. Phishing frauds may be the most widespread cybercrime used today. Machine learning focuses on computer algorithms which improves automatically through experience. Machine learning methods were utilized to detect phishing URLs that typically evaluates an URL based on a feature or set of features extracted from it. This paper presents an approach to identify phishing websites using trained machine learning models. It also delivers a detailed analysis of phishing attacks with a comparison on machine learning approaches used for analysis and classification of phishing and legitimate websites. |
| Databáze: | OpenAIRE |
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