# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 2 Related Works  
The highway deep pyramid convolution neural network (HDP-CNN) is an innovative approach for detecting phishing websites, as described in Ref. [8]. This approach's power stems from its ability to incorporate character- and word-level representation information, allowing it to capture both local and global URL properties. After examining an unbalanced dataset, they found that their approach outperformed others in terms of accuracy, true positive rate, and true negative rate. HD-PCNN outperforms single-feature-based algorithms by leveraging embedded feature information at various granularities. Nonetheless, the study provides little insight into the model's interpretability and generalizability, and it lacks complete dataset information. Overall, their proposed HDP-CNN strategy has shown potential in the realm of phishing detection, providing a more effective solution than current strategies. Ref. [9] proposed a new machine learning-based approach, called LogBERT-BiLSTM, for detecting possible attacks on HTTP requests. The model combines BERT and bidirectional LSTMs to identify anomalies in the data and the proposed approach was evaluated against existing models using the CSIC 2010 and ECML/PKDD 2007 datasets. Additionally, a new dataset of HTTP requests was created for evaluating the model's performance. The experimental results demonstrate that the LogBERT-BiLSTM model consistently achieves detection rates above 95% accuracy on the evaluated datasets. The potential weaknesses of this paper are the use of datasets that are too old to evaluate their model, it is better to use recent datasets, and the lack of details regarding the specific configuration and hyperparameters used in the LogBERT-BiLSTM model. Providing such information would enable better reproducibility and facilitate future comparisons with similar approaches. Additionally, it would be beneficial to compare the proposed approach with other state-of-the-art models in the field of detecting attacks on HTTP requests. Ref. [10] addressed the problem of identifying phishing websites, which deceive victims by giving false content that looks authentic, by proposing character-level BIGRU-attention for phishing classification. They used the BiGRU-Attention model methodology, which considers the characters that come before and after a given character utilizing the BiGRU mechanism, to collect contextual information. The attention mechanism then uses the composition of individual characters to provide scores, allowing the model to differentiate between phishing and authentic URLs. Their model demonstrates many remarkable strengths, even though the dataset utilized for training and evaluation remained unclear. By leveraging the BiGRU and attention processes, the model is better equipped to gather contextual information that improves its ability to differentiate between phishing and legitimate URLs. However, they achieved an impressive outcome; they did not discuss the potential weaknesses or limitations of their model. Identifying and addressing these limitations, such as dataset biases, computational requirements, or generalizability, would provide a more comprehensive evaluation of the model's effectiveness [10]. Ref. [11] proposed an assessment of the lexical, network, and content-based features for detecting malicious URLs using machine learning and deep learning models; a study employing a dataset comprising 66,506 URL records sought to address this issue by evaluating machine learning and deep learning models. The study meticulously created three distinct feature categories, content-based, network-based, and lexical-based, to develop effective detection mechanisms. Employing rigorous feature selection methods like correlation analysis, ANOVA, and the Chi-squared test, the study discerned the most discriminative threats within the dataset.