# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 3 Proposed Approach
### Ensemble Classification  
The core element of the proposed methodology is the ensemble classification phase, as illustrated in Figure 1. This phase specifically entails the binary classification of electronic mail samples to determine their categorization as unsolicited messages. The primary aim of the phase of ensemble classification is to effectively combine characteristics to improve the classification performance the proposed approach. To accomplish this, the approach relies on the use of ensemble learning as opposed to a single classifier. Ensemble learning is a technique that integrates the outcomes of multiple algorithms of machine learning, referred to as base learners, to generate a prediction accuracy that is more consistent, robust, and precise in comparison to individual base learners. The course of action of ensemble learning entails post-processing of the outcomes of the prediction of base learners to obtain the final prediction of categorization. To enhance the effectiveness of the proposed approach, two well-established techniques of ensemble learning, specifically weighted ensemble learning (Method 1) and soft voting ensemble learning (Method 2), were employed. These techniques were chosen based on their proficiency in handling various types of algorithms of machine learning and allowing the independent processing of hybrid characteristics through the use of distinct algorithms of machine learning. For instance, one algorithm can be utilized for the processing of characteristics based on content, while another algorithm can be employed for the processing of characteristics based on text. The primary distinction between Method 1 and Method 2 lies in their respective approaches to combining the outcomes of base learners and determining the outcome of classification.  
The proposed approach introduces an innovative method by utilizing techniques of ensemble learning, which consists of two base learners, to effectively deal with hybrid characteristics. More specifically, the set of hybrid characteristics consists of two subsets: characteristics based on content and characteristics based on text. Each base learner independently processes these subsets concurrently, with the first base learner handling characteristics based on content and the second base learner handling characteristics based on text. This method offers the advantage of utilizing algorithms of machine learning that achieve the highest classification performance for each subset of characteristics as base learners. Consequently, the model of combination analyzes the outcomes of base learners to determine the final prediction of categorization. The selection of base learners for Method 1 and Method 2 is a complex undertaking due to the extensive range of algorithms of machine learning that are suitable for binary classification.  
The proposed approach focuses exclusively on techniques of machine learning and deep learning that have previously been utilized in the identification of fraudulent emails, which have demonstrated commendable performance. A collection of machine learning algorithms was mainly established to restrict the potential choices, consisting of the following:  
* Function-based Logistic Regression (LR).
* Support Vector Machine (SVM).
* Bayes Point Machine (BPM).
* Neural Network (NN).
* Boosted Decision Tree (BDT).