  The ever-evolving ways attacker continues to im prove their phishing
techniques to bypass existing state-of-the-art phishing detection methods pose
a mountain of challenges to researchers in both industry and academia research
due to the inability of current approaches to detect complex phishing attack.
Thus, current anti-phishing methods remain vulnerable to complex phishing
because of the increasingly sophistication tactics adopted by attacker coupled
with the rate at which new tactics are being developed to evade detection. In
this research, we proposed an adaptable framework that combines Deep learning
and Randon Forest to read images, synthesize speech from deep-fake videos, and
natural language processing at various predictions layered to significantly
increase the performance of machine learning models for phishing attack
detection.
