A novel phishing detection approach using Fuzzy Logic and Deep Learning

People are switching from traditional shopping to internet commerce as Internet access increases quickly. So nowadays people are becoming more dependent on e-commerce-based websites. On the other hand, instead of robbing businesses like banks and stores, modern thieves now use the anonymous internet architecture to track down their victims online. Hackers are employing new strategies, such as phishing, to deceive their victims by creating fake websites to collect sensitive data, such as account numbers, usernames, and passwords. Due to the changing nature of phishing attack structure, which primarily targets the vulnerabilities of web users, determining if a website is authentic or phishing is a very challenging task. In this project, our aim is to improve the existing phishing detection solution with state-of-the-art deep learning models and fuzzy decision-making techniques with a goal to make it ready for commercialization.

Faculty Supervisor:

Nashid Shahriar

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Computer science

Sector:

Cyber Security; Technology

University:

University of Regina

Program: