# Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks
## 2 Literature Review
### Deep Learning-Based Methods  
A deep learning model using an exposed neural network was proposed. In this model, a raw short character string was taken as an input, and features were extracted and classified via character-level embedding using a convolutional neural network for the detection of phishing URLs. The string included a security input, potential malicious URLs, file paths, etc. Using a deep learning model, deep automated features were designed, and malicious URLs were extracted, yielding 5% to 10% better performance than the state-of-art results [29]. In another study, several new malware classification architectures were proposed, using a long short-term memory (LSTM) language model and a gated recurrent unit (GRU) language model. A single-stage malware classifier was proposed based on a character-level convolutional neural network (CNN). The researchers comparedthe results and showed that LSTM attained better results with temporal max pooling and that logistic regression offered 31.3% improved results. It was better at capturing long-term dependencies, and neural language models helped increase the performance of malware detection [30]. GNNs have shown great potential by leveraging graph structures, where key properties such as connectivity and diagnosability are crucial for the performance of GNN models, as they impact the structural integrity of the input graphs. Research on the diagnosability of various graphs provides valuable insights into the robustness and fault tolerance of GNN-based models. Relevant studies that could enhance the models' performance in phishing detection include work on the diagnosability of bubble-sort star graphs under the PMC and MM* models [1]. Additionally, iterative processes for entity relationship and business process model extraction, as discussed in [PERSON] and [PERSON]'s work on iMER [2], offer valuable techniques for improving data pattern extraction, which can be adapted to enhance the efficiency of phishing detection models. In a previous study, an approach was proposed to automatically extract URL features from phishing websites using deep neural networks. A CNN model was employed to extract the character-level spatial features from the embedded URL, bi-directional LSTM was used to classify them and generate them for the detection of the word level, and a temporal attention-based hierarchical RNN model was employed for the representation of the URL. For the detection of phishing URLs, a fused feature representation of a three-layer CNN and a multilayer perceptron (MLP) were used [31]. A deep-learning-based framework was proposed for phishing URL detection, in which a browser plug-in is capable of determining whether the phishing risk in real-time is high or low. Based on the user's visits to web pages, it provides the user with a warning message. Real-time prediction servers were combined to generate multiple strategies to improve the detection of threats to website users through URLs. The used framework was capable of recognizing false alarms, reducing the calculation time of prediction, and detecting the whitelist and blacklist based on machine learning predictions using multiple datasets. The RNN-GRU-based model helped the framework attain an accuracy rate of 99.18%, demonstrating its feasibility [1]. In another previous study, an adaptive, self-structuring artificial neural network was applied for the classification of true and false URL detection. Phishing URL-based features are continuous problems in detection and determination, and the types of web pages are constantly changing. The researchers tried to resolve these issues by automatically processing the structure of the network, which showed a high acceptance of noisy data, fault tolerance, and a high prediction accuracy, achieved by setting different epoch values in the several experiments performed [32].