# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 2 Related Works
The comprehensive comparison of various ML and DL models, considering standard assessment criteria, culminated in a standout revelation: The naive Bayes model exhibited exceptional prowess, boasting a striking 96% accuracy rate in identifying hazardous URLs. The authors achieved significant results, but still there exists a limitation in their model; their model does not detect the ever-evolving nature of URLs and websites that criminals use for deceiving the user and they do not extract keyword features from their dataset. [PERSON]. [12] introduced a novel approach for detecting malicious URLs, acknowledging the increasing prevalence of fraudulent websites causing substantial losses worldwide. They developed a joint neural network algorithm that amalgamates a bidirectional independent recurrent neural network (Bi-IndRNN), a capsule network (CapsNet), and an attention mechanism. Their model incorporated static embedding vector features of URLs at both character and word levels, trained using word2 vec, alongside extracting fingerprint information from the binary files' texture. This fusion of extracted characteristics fed into the joint neural network model. Utilizing multi-head attention mechanisms and Bi-IndRNN, they extracted contextual semantic features, followed by CapsNet employing dynamic routing for deeper semantic understanding. Ultimately, a sigmoid classifier facilitated classification. The approach yielded significantly improved classification accuracy compared to prior studies, reaching 99.89%. Despite this success, the study did not integrate dynamic and static URL features and overlooked the model's time cost, representing potential areas for enhancement despite the exceptional classification accuracy achieved. Ref. [13] proposed phishing detection using machine learning-based URL analysis. In our increasingly online-dependent lives, cyber threats like phishing attacks using deceptive URLs have become pervasive, exploiting human vulnerabilities rather than software flaws. Detecting these malicious URLs requires employing various machine learning techniques. Researchers continually strive to refine these methods, examining diverse algorithms, datasets, and URL features to enhance accuracy. They aim to create a survey tool that not only keeps researchers updated on the latest advancements but also aids in developing more precise phishing detection models. Such advancements are crucial in fortifying defenses against evolving cyber threats, ensuring heightened online security for individuals and businesses [13]. Ref. [14] proposed malicious URL attack type detection using multiclass classification. Malicious URLs continue to be a prevalent cybersecurity threat. Using blacklists to identify malicious URLs is a common technique. Blacklists keep track of previously identified dangerous URL reputations. However, these lists fall short when it comes to identifying newly created malicious URLs. To detect malicious URLs, machine learning algorithms are trained in modern research. In this paper, they contributed to the multiclass classification setting by employing URL-based features for the detection of malicious URLs. They concentrated on three common forms of URL attacks: malware, spam, and phishing. Their research applied to new or current anti-phishing, anti-spam, and anti-malware detection platforms as an additional tool. The efficacy of the subsequent inescapable learners was examined using CatBoost, LightGBM, AdaBoost, Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). Priority features like bag of words segmentation, Kullback-Leibler divergence (KL divergence), and other word-based features were among them. Using 126,983 URLs from benchmark datasets, they trained these algorithms, and all four learners produced overall accuracy values greater than 0.95. The authors achieved significant results, but they suggest ensemble learners, deep learning, and online learner mechanisms to detect malicious URLs. Ref.