Online meta-learning firewall to prevent phishing attacks

Abstract

Phishing is the most well-known act of deceiving the Internet users, in which the ‘perpetrator’ plays a credible entity. This is done by misusing the inadequate protection provided by electronic tools, and by exploiting the ignorance of the user-object, in order to illegally obtain personal data, such as sensitive private information and passwords. This research proposes the online meta-learning firewall to prevent phishing attacks. It is a highly innovative and fully automated active safety tool that uses a long short-term memory meta-learner algorithm. This method can learn to efficiently classify using a small number of samples. At the same time, it can converge with a fairly small number of steps. The proposed system is an improvement on the k-nearest neighbor with self-adjusting memory algorithm, which is inspired by the model of short and long-term memory. The purpose of the system is to understand the nature of an unknown situation and to classify it, based on the most relevant characteristics that come directly from the unknown environment.