An IBGWO optimized feature selection framework for sentiment analysis-based fraudulent website detection using MLRNN

Detecting fraudulent websites is critical to ensuring network security and protecting users from online threats. Advanced machine learning algorithms are introduced to analyse patterns, anomalies, and behaviours associated with malicious online activity, which helps detect fraudulent websites. This method only detects scam sites, and there is no practical way to detect new scam websites quickly. The previous methods were not better at handling large amounts of data and could not show accurate results. In addition, fraudulent website detection poses significant threats to online security, and often struggle with high-dimensional feature spaces, feature redundancy and limited generalization to unseen attacks. To resolve these issues, this paper presents a new method for detecting fraudulent websites based on Improved Binary Grey Wolf Optimization feature subset selection based on the Multi-Layer Recognition Neural Network (MLRNN) to enhance quality features and improve overall accuracy. First, fraudulent website detection was collected from the Kaggle repository. After preprocessing, the second step is selecting the features using Attributes-based Recursive Feature Elimination (A-RFE) to select relevant features to improve model performance and minimize overfitting. Next, Subset Feature Selection involves selecting a suitable subset of features from the original dataset and a fitness function that evaluates the quality of the chosen subset using Improved Binary Grey Wolf Optimization (IBGWO). After feature selection, the data is validated using K-10-fold cross-validation, which involves repeating the training and testing procedure 10 times across different data distributions. K-10-fold cross-validation offers a sufficient approximation of the model performance. Multi-Layer Recognition Neural Network (MLRNN) is used for the sentiment analysis process due to its ability to recognize complicated data links and patterns. This model can effectively understand users' emotion-related features to detect fraud websites efficiently. The proposed model evaluated on the web page phising detection dataset contains 11,430 URLs with 89 features. Based on the confusion matrix, the simulation results can be used to analyse precision, recall, accuracy, error rate, f-measure, and time complexity, showing higher accuracy than previous methods.