Real-Time Detection of Malicious URLs by Utilizing Machine Learning Techniques

It comprises several approaches in both machine learning and deep learning for detecting malicious URL addresses and phishing attacks. Major approaches compared in the field include SVM, CNN, and Random Forest; however, it is seen that most of these processes yield enhanced accuracy and efficiency of classification with Random Forest. Innovation methods including N-Gram algorithms, hybrid gram techniques, tokenization, and vectorization are checked for improvement of the accuracy of detection of malicious URL addresses and phishing attacks based on different datasets. Among these, BERT and LSTMs have been exploited to achieve better real-time detection along with Conditional GANs. Further studies indicates that this process has minimized feature selection issues and hence improved the accuracy and speed of the model. These studies emphasize that further work in this area will be significant in terms of more integration of larger datasets and hybrid models for enhancing its measures of cybersecurity.