Version 1
: Received: 27 February 2024 / Approved: 27 February 2024 / Online: 27 February 2024 (15:39:23 CET)
How to cite:
Ige, T.; Kiekintveld, C.; Piplai, A. Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework. Preprints2024, 2024021557. https://doi.org/10.20944/preprints202402.1557.v1
Ige, T.; Kiekintveld, C.; Piplai, A. Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework. Preprints 2024, 2024021557. https://doi.org/10.20944/preprints202402.1557.v1
Ige, T.; Kiekintveld, C.; Piplai, A. Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework. Preprints2024, 2024021557. https://doi.org/10.20944/preprints202402.1557.v1
APA Style
Ige, T., Kiekintveld, C., & Piplai, A. (2024). Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework. Preprints. https://doi.org/10.20944/preprints202402.1557.v1
Chicago/Turabian Style
Ige, T., Christopher Kiekintveld and Aritran Piplai. 2024 "Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework" Preprints. https://doi.org/10.20944/preprints202402.1557.v1
Abstract
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.
Keywords
Phishing; Random Forest; Deep Learning; Recurrent Neural Network; Long Short-Term Memory; Speech Synthesis; Vision Synthesis; Phishing Detection Framework; Adaptive Framework
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
