This survey paper provides a comprehensive overview of deep learning-based anomaly detection (DAD) techniques, aiming to bridge the gap in existing literature by offering a structured and detailed review. The paper is divided into several sections, each addressing different aspects of DAD: 1. **Introduction**: It introduces the concept of anomalies and novelty detection, highlighting their importance in various applications. It also discusses the limitations of traditional algorithms and the advantages of deep learning in handling complex data structures. 2. **Motivation and Challenges**: It outlines the motivations behind using deep learning for anomaly detection, including the need for large-scale data processing and the ability to learn hierarchical features automatically. 3. **Related Work**: It reviews existing surveys and studies on DAD, noting the scarcity of comprehensive reviews and the lack of comparative analysis of deep learning architectures. 4. **Contributions**: The paper's main contributions are outlined, emphasizing its comprehensive coverage of state-of-the-art DAD techniques and real-world applications. 5. **Organization**: The structure of the paper is described, with sections dedicated to different aspects of DAD, including input data nature, availability of labels, training objectives, and output types. 6. **Applications of DAD**: This section discusses various application domains where DAD techniques have been applied, such as intrusion detection, fraud detection, malware detection, medical anomaly detection, social network analysis, log anomaly detection, IoT big data, industrial operations, time series analysis, and video surveillance. 7. **Deep Anomaly Detection Models**: It provides an in-depth discussion of different DAD models, including supervised, semi-supervised, hybrid, and one-class neural network approaches, detailing their assumptions, model architectures, computational complexity, advantages, and disadvantages. The paper aims to serve as a valuable resource for researchers and engineers interested in leveraging deep learning for anomaly detection, providing a detailed and structured overview of the field.This survey paper provides a comprehensive overview of deep learning-based anomaly detection (DAD) techniques, aiming to bridge the gap in existing literature by offering a structured and detailed review. The paper is divided into several sections, each addressing different aspects of DAD: 1. **Introduction**: It introduces the concept of anomalies and novelty detection, highlighting their importance in various applications. It also discusses the limitations of traditional algorithms and the advantages of deep learning in handling complex data structures. 2. **Motivation and Challenges**: It outlines the motivations behind using deep learning for anomaly detection, including the need for large-scale data processing and the ability to learn hierarchical features automatically. 3. **Related Work**: It reviews existing surveys and studies on DAD, noting the scarcity of comprehensive reviews and the lack of comparative analysis of deep learning architectures. 4. **Contributions**: The paper's main contributions are outlined, emphasizing its comprehensive coverage of state-of-the-art DAD techniques and real-world applications. 5. **Organization**: The structure of the paper is described, with sections dedicated to different aspects of DAD, including input data nature, availability of labels, training objectives, and output types. 6. **Applications of DAD**: This section discusses various application domains where DAD techniques have been applied, such as intrusion detection, fraud detection, malware detection, medical anomaly detection, social network analysis, log anomaly detection, IoT big data, industrial operations, time series analysis, and video surveillance. 7. **Deep Anomaly Detection Models**: It provides an in-depth discussion of different DAD models, including supervised, semi-supervised, hybrid, and one-class neural network approaches, detailing their assumptions, model architectures, computational complexity, advantages, and disadvantages. The paper aims to serve as a valuable resource for researchers and engineers interested in leveraging deep learning for anomaly detection, providing a detailed and structured overview of the field.