# A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection
## 1 Introduction
Then, a defined formula using the ratio of the two true and two false amounts can be utilized to assess a model's performance on a particular data set. One of the main issues encountered during machine learning data classification in general [23; 24], and more specifically in malicious websites' classification [25], is the class-imbalanced dataset. In the class-imbalanced issue, a number of classes or a specific class can contain a more instances than another class or group of classes. This means that, in th training stage, the machine learning method becomes biased toward this specific class or other classes with a large number of instances. This can produce a high result for the dominating class or classes, with the results of other classes being completely disregarded. A number of data-balancing strategies are suggested in the literature. Some common strategies are under-sampling, SMOTE, and over-sampling, which have generally been used for imbalanced data classification [26], and specifically for malicious websites classification [25]. However, the biases in the original dataset of malicious websites might keep the data balanced. Moreover, quality assurance in data-balancing is expensive work, and finding an effective data-balancing method is a challenging task, which must prevent overlap between synthesized samples of classes [27]. In this research work, we propose a decision-fusion-based approach that builds a decision-fusion ensemble learning (DFEL) model by combining the decisions of three effective ensemble classifiers, namely, a gradient boosting (GB) classifier, extreme gradient boosting (XGB) classifier, and random forest (RF) classifier. The approach exploits the advantages of these classifiers when used for for class-imbalanced data and data with statistical noise, as in the case of malicious website detection. We utilize a weighted majority-voting rule to generate the final decisions of developed model. The main contributions of the proposed approach can be summarized as follows:* Improving the accuracy of malicious website detection by exploiting the diversity of boosting (i.e., GB and XGB) and bagging (i.e., RF) techniques. In boosting, the approach can create sequential models by combining weak learners into strong learners, where the final model has the highest accuracy. Furthermore, in bagging, the approach can create different training subsets from a sample training set using replacement and the output of the final model is based on the majority voting. * Reducing the class-imbalanced and over-fitting problems in malicious website classification due to the regularization ability of GB, XGB, and RF classifiers. * Proposing a weighted soft voting rule to fuse the final classification scores utilizing the competence of well-calibrated and diverse classifiers such as the base classifiers in the approach. Furthermore, evaluating and comparing the accuracy of the proposed DFEL model with its base classifiers and some recent related work. The rest of the paper is organized as follows: Section 2 presents the methods and approaches of the related work. Section 3 offers an explanation of the materials and methods used in the research work, including a description of the dataset, methods of the proposed approach, and evaluation metrics. The experiments and results, along with a discussion, are introduced in Section 4. Section 5 summarizes the conclusions and future work.