engleski

An overview of machine learning algorithms for detecting phishing attacks on electronic messaging services

Phishing attacks have become today one of the most common security breaches performed on different communication channels. Their goal is to direct users to malicious websites or to infect a user’s computer as a means to acquire personal or sensitive data for later misuse. Phishing is often the first step in the process of cybercrime, and in order to be able to recognize potential attacks and adequately protect users, it is necessary to understand the underlying principles of attack strategies. Therefore, applying machine learning for training a system that would recognize phishing messages would be essential for increasing the level of security from cyberattacks. The aim of this paper is to give an overview of machine learning techniques used for the detection of phishing (and spam) e-mails, focusing mainly on regression and classification algorithms. In addition to the mentioned techniques, an analysis of datasets that are used for training of systems for detecting phishing attacks (and spam) is presented with regard to their size, language and accuracy scores. Different types of phishing messages are analyzed as well in this paper.

information security ; information privacy ; machine learning ; phishing ; attack detection ; spam ; electronic messages

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