# A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection
## 2 Related Work
A two-step method for the detection of benign and malicious URLs was introduced by [PERSON] et al. [33]. An algorithm called \"enhanced probing classification of malicious URLs (EPCMU)\" is used as the first step and an NB classifier is used as the second step to find malicious URLs. The first step involves the detection task and the second is used for classification. The input URLs are thoroughly examined by the EPCMU. The system flags the URLs as malicious if they exhibit any characteristics of a malicious websites or appear on the blacklisted websites. Otherwise, more checks are performed. In the EMPCU, a collection of URLs serves as input to the NB classifier during the classification stage. This determines whether the set URLs are genuine or malicious. An auto-encoder model was used by [PERSON] et al. [34] to differentiate between benign and malicious websites. Three layers (input, hidden, and output) make up the structure of the auto-encoder model. The data of legitimate webpages were gathered from the dataset created by the Canadian Institute for Cyber-security, while the phishing data of webpages were gathered from the dataset generated by the open-source Phish Tank. The final dataset contained a total of 16 features and 10,000 instances. The authors cleaned the missing extracted values in the preprocessing stage. The effectiveness of the auto-encoder-based model is compared to DT and SVM. The authors' model attained 91.24% accuracy. The DT and SVM methods delivered an accuracy of 86.1% and 88.4%, respectively. The contributions and results of important related research have confirmed the ability and applicability of these methods and techniques for detecting benign and malicious websites. Recently, [PERSON] et al. [25] proposed an approach using DT, RF, SVC, LR, and Stochastic Gradient Decent (SGD) classifiers and achieved 94.19% accuracy for imbalanced datasets. The results for data balancing in [25] are discarded because the authors balanced the dataset before splitting it into training and test sets. This means that the distribution of augmented instances in the test set has almost the same distribution of original instances in the training set and increases the accuracy. [PERSON] et al. [28] applied RF, GB, DT, and deep neural network (DNN) methods to obtain an accuracy of up to 96.4%. [PERSON] et al. [29] achieved 90% accuracy using the kAYO technique. Adaptive Boosting (AB), Extra Trees (ET), RF, GB, Bagging Classifier (BC), LR, and k-NN were used by [PERSON] et al. [30] to obtain an accuracy of up to 89%. [PERSON] et al. [32] proposed an approach using a 1D-CNN to attain 94.31% accuracy. [PERSON] et al. [34] used an auto-encoder, DT, and SVM to attain 91.24% accuracy. [PERSON] et al. [35] applied the k-NN method to website features and attained 95% accuracy. [PERSON] et al. [36] and [PERSON] et al. [37] proposed methods using RF to obtain 95% accuracy for both studies. [PERSON] et al. [38] achieved 92% accuracy by using an RF classifier. [PERSON] et al. [39] proposed a multilayer CNN and attained an accuracy of 91%. [PERSON] et al. [40] used an NB classifier and 96% accuracy was obtained. [PERSON] and [PERSON] [41] proposed an approach using LightGBM, DT, SVM, k-NN, LR, multilayer perceptron (MLP), RF, and XGB to gain 96% accuracy. The results of previous studies show that the existing methods have several strengths, such as a good performance on malicious website detection tasks. They are simple and can be regularized to decrease the chance of over-fitting. Their outputs can be interpreted easily, and some of them do not need to scale the feature values and can be used for both non-linear and linear features. However, they have limitations in their ability to produce an effective trained model with reduced variance and bias, and improved classification results. Moreover, a common limitation of the existing work is the ability to reduce the effect of class-imbalanced and highly correlated features on the accuracy and performance of malicious website classification.