  Trusting the accuracy of data inputted on online platforms can be difficult
due to the possibility of malicious websites gathering information for unlawful
reasons. Analyzing each website individually becomes challenging with the
presence of such malicious sites, making it hard to efficiently list all
Uniform Resource Locators (URLs) on a blacklist. This ongoing challenge
emphasizes the crucial need for strong security measures to safeguard against
potential threats and unauthorized data collection. To detect the risk posed by
malicious websites, it is proposed to utilize Machine Learning (ML)-based
techniques. To this, we used several ML techniques such as Hist Gradient
Boosting Classifier (HGBC), K-Nearest Neighbor (KNN), Logistic Regression (LR),
Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Light
Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM) for
detection of the benign and malicious website dataset. The dataset used
contains 1781 records of malicious and benign website data with 13 features.
First, we investigated missing value imputation on the dataset. Then, we
normalized this data by scaling to a range of zero and one. Next, we utilized
the Synthetic Minority Oversampling Technique (SMOTE) to balance the training
data since the data set was unbalanced. After that, we applied ML algorithms to
the balanced training set. Meanwhile, all algorithms were optimized based on
grid search. Finally, the models were evaluated based on accuracy, precision,
recall, F1 score, and the Area Under the Curve (AUC) metrics. The results
demonstrated that the HGBC classifier has the best performance in terms of the
mentioned metrics compared to the other classifiers.
