A Hybrid CNN-Brown-Bear Optimization Framework for Enhanced Detection of URL Phishing Attacks

Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial services. After the first reported incident in 1995, its impact keeps on increasing. Also, during COVID-19, due to the increase in digitization, there is an exponential increase in the number of victims of phishing attacks. Many deep learning and machine learning techniques are available to detect phishing attacks. However, most of the techniques did not use efficient optimization techniques. In this context, our proposed model used random forest-based techniques to select the best features, and then the Brown-Bear optimization algorithm (BBOA) was used to fine-tune the hyper-parameters of the convolutional neural network (CNN) model. To test our model, we used a dataset from Kaggle comprising 11,000+ websites. In addition to that, the dataset also consists of the 30 features that are extracted from the website uniform resource locator (URL). The target variable has two classes: “Safe” and “Phishing.” Due to the use of BBOA, our proposed model detects malicious URLs with an accuracy of 93% and a precision of 92%. In addition, comparing our model with standard techniques, such as GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), ANN (Artificial Neural Network), SVM (Support Vector Machine), and LR (Logistic Regression), presents the effectiveness of our proposed model. Also, the comparison with past literature showcases the contribution and novelty of our proposed model.