Malicious URL Detection Using Machine Learning and Deep Learning Hybrid Models

Abstract

The proliferation of bad URLs has posed a serious challenge to cybersecurity, as conventional detection techniques are unable to keep up with the swift changes in online threats. The goal of this study is to investigate how hybrid models that integrate machine learning (ML) and deep learning (DL) might improve the reliability and accuracy of URL detection systems. To enhance the detection of malicious URLs, we suggest three hybrid architectures: CNN+LSTM, CNN+RNN, and LightGBM+BERT. These designs take advantage of the advantages of both paradigms. The goal of the project is to improve cybersecurity frameworks detection capabilities while also looking into new defensive strategies against dynamic cyberthreats. In order to analyse important URL characteristics, such as anomalous URL structures, Google index status, short URL detection, suspicious patterns, and length-based data like hostname length, first directory length, and top-level domain length, our solution combines feature extraction approaches. The suggested models are better able to identify if a URL is malicious or benign by dissecting these characteristics. The experimental results show that our methods beat conventional ML and DL approaches in terms of accuracy and efficiency. The hybrid models are trained and assessed using a dataset of real-world URLs. Combining long short-term memory networks (LSTMs) for sequence learning and convolutional neural networks (CNNs) for feature extraction works especially well for capturing temporal and spatial patterns within URLs. According to our research, these hybrid models significantly improve internet users’ overall security by facilitating the early detection of cyberthreats. Future advancements in URL detection are made possible by this research, which also creates new opportunities for incorporating cutting-edge technologies like blockchain for safe URL verification.