Abstract
Malicious assaults, with an emphasis on URLs, are detected using a new technique that makes use of machine learning techniques. We use hybrid machine learning models in conjunction with ensemble approaches for Natural Language Processing (NLP). To extract pertinent information, we preprocess a dataset that includes both malicious and genuine URLs. We improve our models' accuracy and efficiency by using strategies like Grid Search Hyper Parameter Optimisation and Canopy feature selection. Evaluation measures that show the effectiveness of our method include precision, accuracy, recall, F1-score, and specificity. Our hybrid machine learning system, which incorporates natural language processing (NLP), performs better than current models, providing strong protection against malevolent threats and improving cyber security, according to comparative analysis. . Keywords: Machine learning, Natural Language Processing (NLP), Cybersecurity, Canopy feature selection, Grid Search Hyperparameter Optimization, evaluation metrics
Published Version
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