# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 4 Experiments and Results
### Comparison of Our Approach's Results with Existing Works
\begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Insights** & **Results** & **Methods Used** \\ \hline \multirow{6}{*}{[45]} & The provided paper does not mention ensemble classification in phishing detection. The paper focuses on brute-force detection using ensemble classification. & The stacking algorithm achieved the highest accuracy, precision, recall, and F1. Test results: 94.87\% accuracy, 99.94\% precision, 98.82\% recall, 99.37\% F1. & Four types of ensemble classifiers were used. The stacking algorithm achieved the best test results. \\ \cline{1-1} \cline{2-3}  & The manuscript introduces a composite ensemble learning model, consisting of Decision Trees (DTree) and Naive Bayes (NBayes), to classify phishing instances. This model demonstrated superior performance compared to the hybrid model, as evidenced by the evaluation of various performance measures. & The ensemble model exhibited superior performance when compared to the hybrid model in terms of the utilized measures. In addition, the DTree and STACKING techniques demonstrated exceptional performance in comparison to the other models. & A composite learning model consisting of Decision Tree (DTree) and Naive Bayes (NBayes) was utilized. The STACKING method was employed, with DTree serving as the foundational learner. \\ \cline{1-1} \cline{2-3}  & This paper discusses the use of ensemble learning models, specifically AdaBoost, CatBoost, and Gradient Boosting & The AdaBoost Classifier algorithm demonstrated the best result for predicting phishing websites. The average ROC AUC score for the AdaBoost Classifier algorithm was 99\%. & Ensemble learning algorithms (AdaBoost, CatBoost, Gradient Boosting Classifier). Exploratory Data Analysis (EDA). \\ \cline{1-1} \cline{2-3}  & The paper discusses the use of ensemble models, such as CatBoost Classifier, for detecting phishing attacks on web networks. & The proposed ensemble model achieved 99.02\% accuracy with 10-fold cross-validation. Machine learning-based classification techniques provided an accurate method of classification. & A variety of machine learning models were employed to identify instances of phishing. The employed models encompassed K-means, Random Forest, Decision Tree, the CatBoost classifier, the LightGBM classifier, AdaBoost, and the voting classifier. \\ \cline{1-1} \cline{2-3}  & The proposed ensemble model achieved for phishing detection that combines cross-validation. Machine learning-based classification techniques provided an accurate method of classification. & Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR). Ensemble classifiers like Extreme Gradient Boosting (XGBoost) and Adaptive Boosting (AdaBoost). \\ \cline{1-1} \cline{2-3}  & Random Forest ensemble techniques combined with k-means clustering. Wrapper-based and filter-based methods for feature selection. & Random Forest ensemble techniques combined with k-means clustering. Wrapper-based and filter-based methods for feature selection. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of phishing detection approaches.