Ensemble Based Detection of Phishing URLs Using Hybrid, Deep Learning and Machine Learning Models

Abstract: Phishing attacks pose a serious cybersecurity threat, requiring advanced detection mechanisms. This study proposes an ensemble-based phishing Uniform Resource Locator(URL) detection framework integrating both machine learning and deep learning models. The first phase employs Adaboost, Naïve Bayes(NB), Random Forest(RF), Logistic Regression(LR), Support Vector Machine(SVM), Artificial Neural Network(ANN), Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short TermMemory(LSTM) and Stacked Gated Recurrent Unit(Stacked GRU), combined using voting ensemble. The second phase includes detection with hybrid deep learning models, including Neural Network -Long Short Term Memory(NN_LSTM), StackedGated Recurrent Unit-Convolutional Neural Network-Long Short Term Memory(StackedGRU_CNN_LSTM), Deep Belief Network -StackedGated Recurrent UnitTransformer(DBN_StackedGRU_Transformer), Autoencoder+Convolutional Neural Network-Long Short Term Memory+BiGated Recurrent Unit(AutoencoderCNNLSTMBiGRU), and Multi LayerPerceptron-Bi-Long Short Term MemoryConvolutional Neural Network-Gated Recurrent Unit(MLP_BiLSTM_CNN_GRU), utilizing stacking and a host of other ensemble methods like Voting,Weighted Averaging, Confidence-Based Stacking, Gated Mixture of Experts, Neural Greedy Selector, Stacked with Featuresfor improved classification. Performance evaluation using accuracy, precision, recall, and F1- score shows that ensemble learning significantly enhances phishing detection accuracy, making it a robust cybersecurity solution.