# A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection
## 3 Materials and Methods
### Methodology of Proposed Approach
#### 3.2.1 Gradient-Boosting (GB) Classifier Method
The other advantage of the decision fusion-based approach is the diversity of GB, XGB, and RF models in reducing the effect of over-fitting and class-imbalanced problems during training development. ## 5 Conclusions and Future Work

In the cyber-security field, machine-learning-based applications play an essential role in the detection of malicious data, and can also be used to analyze interactive websites and provide efficient solutions. Fusing different data sources and decisions is one of the outstanding strategies to increase the accuracy of machine learning techniques in several applications. This paper proposes an effective and efficient decision-fusion-based approach that fuses the decisions of GB, XGB, and RF classifiers and builds a decision-fusion ensemble learning (DFEL) model. The DFEL is able to classify webpage features and detect the URLs of malicious websites. The final decision is calculated by fusing the classification scores of the three classifiers using a weighted soft-voting method. The outcome could be classified as malicious or benign based on the significant features of the website URLs. An extensive set of experiments is performed on a large public benchmark dataset of malicious and benign website URLs using a 10-fold cross-validation test and different test set ratio. The experimental results show that the proposed fusion method achieved a higher accuracy than the individual models and the current related work. The experimental results also investigate the effect of highly correlated features on the accuracy of the developed model and decrease the detection time by reducing the highly correlated features in the model input. One limitation of this approach is the unavailability of a large class-balanced dataset that can be used to train deep learning models and for malicious websites classification. Consequently, in future work, we will use data-augmentation techniques to extend the size of the minority class in the training set and investigate the advantages of deep learning \begin{table}
\begin{tabular}{c c c c} \hline
**Authors [Reference]** & **Year** & **Methods/Techniques** & **Accuracy** \\ \hline [PERSON] et al. [29] & 2017 & kAYO & 90\% \\ \hline [PERSON] et al. [35] & 2018 & k-NN & 95\% \\ \hline [PERSON] et al. [30] & 2019 & AB, ET, RF, GB, BG, LR, and & 89\% \\  & & k-NN & 89\% \\ \hline [PERSON] et al. [36] & 2020 & RF & 95\% \\ \hline [PERSON] et al. [37] & 2020 & RF & 95\% \\ \hline [PERSON] et al. [32] & 2020 & 1D-CNN & 94.31\% \\ \hline [PERSON] et al. [28] & 2020 & RF, GB, DT, and DNN & 96.4\% \\ \hline [PERSON] et al. [38] & 2021 & RF & 92\% \\ \hline [PERSON] et al. [39] & 2021 & Multilayer CNN & 91\% \\ \hline [PERSON] et al. [40] & 2022 & NB & 96\% \\ \hline [PERSON] and [PERSON] [41] & 2022 &
\begin{tabular}{c} LightGBM, DT, SVM, k-NN, \\ LR, MLP, RF, and XGB \\ \end{tabular} & 96\% \\ \hline [PERSON] et al. [34] & 2022 & Auto-encoder, DT, and SVM & 91.24\% \\ \hline [PERSON] et al. [25] & 2022 & DT, RF, SVC, LR, and SGD & 94.19\% \\ \hline This work & 2023 & DFEL & 98.50\% \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of the results of the accuracy of the proposed DFEL model and the important studies in the literature review. models on the problem field. Furthermore, we plan to build a model integrated with Fuzzy logic to improve the reliability of the detection approach. Conceptualization, A.A. and A.G.; methodology, A.A. and A.G.; software, A.A. and A.G.; validation, A.A. and A.G.; formal analysis, A.A. and A.G.; investigation, A.A. and A.G.; resources, A.A. and A.G.; data curation, A.A. and A.G.; writing--original draft preparation, A.A. and A.G.; writing--review and editing, A.A. and A.G.; visualization, A.A. and A.G.; supervision, A.A. and A.G.; project administration, A.A. and A.G.; funding acquisition, A.A. and A.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.