A maximum entropy classification scheme for phishing detection using parsimonious features
| Autor: | Adebayo Omotosho, Olumide Babatope Longe, Joyce Ayoola, Peace Ayegba, Emmanuel Oluwatobi Asani, Paul Danquah |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Principle of maximum entropy Feature vector Machine learning computer.software_genre Classification Phishing Electronic mail Support vector machine Naive Bayes classifier Identification (information) Information sensitivity Parsimonous features Artificial intelligence Social engineering Electrical and Electronic Engineering business computer |
| Popis: | Over the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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