Advancements In Cybersecurity: A Data Analytics Approach For Proactive Detection And Mitigation Of Malicious Urls

Abstract

The demand for proactive detection and mitigation solutions to protect against harmful actions on the internet is growing due to the sophistication of cyber threats. The aim of this study is to identify This study's objective is to ascertain malicious URLs using a new data analytics strategy that makes use of sophisticated machine learning algorithms. With our method, URLs are analyzed and classified as benign or malicious by combining the capabilities of classic Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN).The dataset offers an extensive training and testing environment since it consists of a wide variety of URLs that have been classified as benign or dangerous. We assess Receiver Operating Characteristic (ROC) curve, F1 score, accuracy, and precision performance of the combined CNN-LSTM and ANN-LSTM models by a comparative study. The outcomes demonstrate how well our method works to differentiate between legitimate and malicious URLs, allowing for proactive threat identification.Besides, we choose the best performing model to use for real-time new URL categorization based on the assessment criteria. Users may confirm the authenticity of URLs and reduce security concerns by integrating this paradigm into a web-based application. Our method provides a scalable and efficient way to counteract cyber threats that are constantly changing in the digital sphere by utilizing deep learning and data analytics.