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This study conducts a comparative analysis of fifty academic papers published between 2018 and 2024, focusing on the application of Machine Learning (ML) and Deep Learning (DL) in Intrusion Detection Systems (IDS). It highlights the limitations of traditional IDS models and emphasizes the shift towards hybrid models that combine ML and DL techniques for improved detection rates and interpretability. The research identifies key trends, popular algorithms, and datasets, while also suggesting future directions

Author

deepikanegi2309

Language

EN