Phishing Detection Using Machine Learning Techniques

Nishitha, U; Kandimalla, Revanth; Mourya Vardhan, Reddy M; Kumaran, U

doi:10.1109/asiancon58793.2023.10270550

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…Nishitha et al [23] compared performances of machine learning algorithms and deep learning for phishing detection classification by implementing KNN, Decision tree, Random Forest, Logistic Regression as machine learning algorithm, convolusional neural network and recurrent neural network as deep learning in which logistic regression and CNN had the best performances with an accuracy of 95% and 96% respectively, albeit the proposed model only uses the URL properties and so couldn't be used for a sophisticated phishing attack that relies on images and video content.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework

Ige,

Kiekintveld,

Piplai

2024

Preprint

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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.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%