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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

Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Amy Waggler, Olukunle Kolade, Bolanle Hafiz Matti

TL;DR

This research 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, and made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model.

Abstract

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

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

TL;DR

This research 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, and made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model.

Abstract

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

Paper Structure

This paper contains 21 sections, 3 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. BACKGROUND STUDY
  3. Email Phishing
  4. Spear Phishing
  5. Voice Phishing (Vishing)
  6. SMS Phishing (Smishing)
  7. Category of Current State of the art Phishing Detection model
  8. Bayesian-Based-Classifier
  9. Non-Bayesian Based Classifier
  10. Decision Tree
  11. Random Forest
  12. Logistic Regression
  13. XGBoost
  14. K-Nearest Neighbor (KNN)
  15. Support Vector Machine (SVM)
  16. ...and 6 more sections

Figures (3)

  • Figure 1: Phishing statistics from 2013 Q3 to 2022 Q3.
  • Figure 2: Comparative Analysis of State-of-the-art Phishing Algorithm for Phishing Detection.
  • Figure 3: Comparative Analysis of State-of-the-art Phishing Algorithm for Phishing Detection.