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A Deep Learningbased Approach to Identifying and Mitigating Network Attacks Within SDN Environments Using Nonstandard Data SourcesMatthew David BantonA thesis submitted in partial fulfilment of the requirements of Liverpool John Moores University for the degree of PhD.November 2020Acknowledgements This thesis was completed following extensive research into Deep Learning, and I would like to express my gratitude to several people who have supported me throughout. First thanks must go to

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What is a deep learning-based approach?

A deep learning-based approach refers to a method of artificial intelligence that uses neural networks with many layers (deep networks) to analyze data and learn from it, allowing for the identification of patterns and making predictions.

Who is required to file a deep learning-based approach?

Researchers and practitioners in fields like computer science, data science, and artificial intelligence who develop or apply deep learning models and methodologies may be required to file a deep learning-based approach, particularly when seeking funding, publishing research, or complying with regulatory standards.

How to fill out a deep learning-based approach?

To fill out a deep learning-based approach, one must detail the problem statement, describe the deep learning model to be used, specify the data sources, outline the training and validation process, and define expected outcomes and metrics for evaluation.

What is the purpose of a deep learning-based approach?

The purpose of a deep learning-based approach is to enable machines to automatically learn and improve from experience without being explicitly programmed, driving advancements in various applications such as image recognition, natural language processing, and autonomous systems.

What information must be reported on a deep learning-based approach?

Information that must be reported includes the model architecture, training data description, preprocessing techniques, performance metrics, results obtained, and any ethical considerations associated with the model's deployment.