The book discusses deep learning models for Intrusion Detection System (IDS). An in-depth comparison is conducted between the sample network intrusion detection model and well-known models. The book consists of 4 chapters. The initial two chapters provide an overview of intrusion detection systems and fundamental principles of deep learning models. The material includes an overview, components, technology, classification, and issues. The upcoming chapters will focus on the experimental and implementation results, constraints, suggestions, and future possibilities of IDS. Enhancements are being made to deep learning models for the purpose of identifying and stopping network breaches in intrusion detection. An innovative intrusion detection model based on Recurrent Neural Networks (RNN) is presented, including comprehensive design and operational information. Oversampling is used to address unbalanced datasets by adjusting the training and testing sets separately. An emphasis is placed on a method to enhance model performance.