# Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models
## 2 Related Work  
In [12], the authors proposed a novel generalized phishing detection system based on an AV binary-modified equilibrium optimizer with k-nearest neighbors (KNN). The system uses a binary version of a modified equilibrium optimizer (AV-BMEO) for feature selection and a K-nearest neighbor machine learning algorithm for classification. [PERSON] et al. in [13] introduced a functional tree meta-learning mechanism for phishing site detection. A functional tree-based (FT) model is highly effective for detecting phishing and legitimate websites with better accuracy, and it is recommended. In [14], researchers proposed a novel method to detect a phishing URL website using a self-attention convolutional neural network (CNN) algorithm. The authors used an imbalanced dataset and a generative adversarial network (GAN) deep learning model to produce data for an imbalanced dataset. Next, they combined the CNN deep learning model and multithread self-attention to build the classifier. Linear and nonlinear space-transformation-based methods have been used [15] for malicious URL detection via feature engineering. In this study, a two-stage distance metric technique was developed for linear transformation and the Nystrom approach for kernel approximation was introduced for both linear and nonlinear transformations. A machine-learning-based predictive model to classify websites as phishing and legitimate was introduced in 2020 [16]. The authors in this study proposed a machine-learning-based system to detect phishing websites. Support vector machines, convolutional neural networks, and K-nearest neighbor machine learning algorithms were used to detect phishing websites. [PERSON] et al. proposed a lightweight URL-based phishing detection method using natural language processing transformers for mobile devices. They applied the artificial neural network (ANNs) model to URL-based and HTML-based website features to distinguish malicious from legitimate URLs, and the proposed method can only be used on mobile devices [17]. In 2018, the authors of [18] implemented a neural-network-based model to detect phishing websites. This model used neural-network-based classification with a simple and stable Monte Carlo algorithm. In [19], a model for detecting spam emails was proposed, which is a hybrid system of neural networks. The proposed approach used a technique that automatically adds the number of emails to corpus datasets. [PERSON] et al. [20] developed a model based on a heuristic-based regression approach combined with a decision tree algorithm and a wrapper feature selection approach to detect phishing websites. The authors of [21] proposed a model that uses the extracted heuristic features from the website itself. The authors used eight different machine learning (ML) algorithms for the evaluation, and the principal component analysis random forest (PCA-RF) algorithm yielded the highest accuracy and image analysis. [PERSON] and [PERSON] [22] developed an intelligent classification model for detecting phishing emails. Publishers mainly used two models: knowledge discovery, which extracts the features from the given string, and data mining, which selects the best classification model. A Java program was used to extract the features from the email header and body, after which a data mining algorithm was applied to the extracted features to determine the algorithm with the best results. In this study, we checked the potential capabilities of LSTM, Bi-LSTM, and GRU for detecting malicious URLs. Compared to RNN, LSTM is a better choice, and RNNs are difficult to train for the input dataset of URLs because they have long-term temporal dependencies. The reason for this is that the gradient decay of the loss function results in nonpolynomial time. By contrast, LSTM uses special units with additional memory cells and can retain information for a long time.