GPH - International Journal Of Computer Science and Engineering <p style="font-family: Comic Sans MS;">Subjects covered in Computer Science and Engineering include: Computer Science;→ Scientific Computing; Wireless Networking; Network Modelling; Computational Science &amp; Engineering; Theoretical Computer Science; Biosystems Engineering; Machine Learning; Systems Biology &amp; Bioinformatics; Biostatistics; Data Mining; Data Analysis; Internet Computing &amp; Web Services; Information System Engineering; Quantum Computing; Nano Computing; Soft Computing; Artificial Intelligence; Digital Signal Processing, Cloud Computing; Robotics; Computer Graphics; Information Science; Medical Image Computing; Natural language Processing; Evolutionary Computation.</p> en-US <p>Author(s) and co-author(s)&nbsp;jointly&nbsp;and severally represent and warrant that the Article is original with the author(s) and does not infringe any&nbsp;copyright or violate any other right of any third parties, and that the Article has not been published&nbsp;elsewhere.&nbsp;Author(s) agree to the terms that the <strong>GPH Journal</strong> will have the full right to remove the published article on any misconduct found in the published article.</p> (Dr. EKEKE, JOHN NDUBUEZE) (Fran) Tue, 20 Jun 2023 06:46:36 +0000 OJS 60 CLASSIFICATION OF PHISHING ATTACKS IN SOCIAL MEDIA USING ASSOCIATIVE RULE MINING AUGMENTED WITH FIREFLY ALGORITHM <p>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.</p> HAMMED, Mudasiru, Jumoke Soyemi ##submission.copyrightStatement## Tue, 20 Jun 2023 06:47:44 +0000