Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection

Md Sultanul Islam Ovi, Md. Hasibur Rahman, Mohammad Arif Hossain

TL;DR

PhishGuard, an optimal custom ensemble model designed to improve phishing site detection, is introduced, demonstrating that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance.

Abstract

Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy. Through advanced feature selection methods such as SelectKBest and RFECV, and optimizations like hyperparameter tuning and data balancing, the model was trained and evaluated on four publicly available datasets. PhishGuard outperformed state-of-the-art models, achieving a detection accuracy of 99.05% on one of the datasets, with similarly high results across other datasets. This research demonstrates that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance.

PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection

TL;DR

PhishGuard, an optimal custom ensemble model designed to improve phishing site detection, is introduced, demonstrating that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance.

Abstract

Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy. Through advanced feature selection methods such as SelectKBest and RFECV, and optimizations like hyperparameter tuning and data balancing, the model was trained and evaluated on four publicly available datasets. PhishGuard outperformed state-of-the-art models, achieving a detection accuracy of 99.05% on one of the datasets, with similarly high results across other datasets. This research demonstrates that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance.
Paper Structure (18 sections, 3 figures, 3 tables, 1 algorithm)

This paper contains 18 sections, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Machine Learning Techniques in Phishing Detection
  4. Ensemble Learning in Phishing Detection
  5. Research Gaps and Future Directions
  6. Methodology
  7. Data Collection and Dataset Description
  8. Data Balancing
  9. Feature Selection and Dimensionality Reduction
  10. Machine Learning Algorithms
  11. Design of the Customized Ensemble Model
  12. Hyperparameter Tuning
  13. Model Evaluation Metrics
  14. Results and Discussion
  15. Performance of Machine Learning Classifiers
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Overview of the PhishGuard development process
  • Figure 2: Comparative analysis of PhishGuard against advanced algorithms al2021optimizedsarasjati2022comparativeabdul2023analysis demonstrates that PhishGuard surpasses all existing approaches on Dataset 01.
  • Figure 3: Comparative analysis of PhishGuard against advanced algorithms khan2020phishingal2021optimizedsarasjati2022comparative demonstrates that PhishGuard surpasses all existing approaches on Dataset 02.