CLASSIFICATION OF PHISHING ATTACKS IN SOCIAL MEDIA USING ASSOCIATIVE RULE MINING AUGMENTED WITH FIREFLY ALGORITHM

Abstract

Social media has significantly grown as a preferred medium of communication for individuals and groups. It is also a tool for disseminating information to the public. Social media offers several advantages, most especially contacting millions of people at the same time. Social media attacks such as phishing evolved as a result of messaging and disseminating capabilities of social media network sites. This challenge of continuous attacks has attracted the attention of many researchers to propose different techniques to detect and classify both phishing attacks and legitimate messages. Studies in the literature revealed that some of the models proposed for phishing attacks may not be perfect to stop adversaries and, there are still different phishing attacks that hindered the robust nature of social media. This study proposed associative rule mining augmented with the Firefly algorithm which attained a high degree of accuracy in both phishing attack messages and legitimate messages.