# An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey

arXiv: https://arxiv.org/abs/2411.16751 · HTML: https://mlsanity.com/paper/2411.16751/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 89 references that are in the map.
- First posted on arXiv 24 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [AI Powered Anti-Cyber Bullying System using Machine Learning Algorithm of Multinomial Naive Bayes and Optimized Linear Support Vector Machine](https://mlsanity.com/paper/2207.11897/) (2022)
- [Adversarial Sampling for Fairness Testing in Deep Neural Network](https://mlsanity.com/paper/2303.02874/) (2023)
- [Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection](https://mlsanity.com/paper/2308.11834/) (2023)
- [Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning](https://mlsanity.com/paper/2401.02052/) (2023)
- [Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework](https://mlsanity.com/paper/2402.17249/) (2024)

## Built on by (most cited first, 1 of 1)

- [Role-Aware Multi-modal federated learning system for detecting phishing webpages](https://mlsanity.com/paper/2509.22369/) (2025)

## Abstract (arXiv)

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their corresponding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model