A survey of deep learning models, datasets, and applications for cyber attack detection.

The frequency and sophistication of cyberattacks have significantly increased over the past several years, it is crucial to mention. This highlights the need for effective and efficient methods for detecting and mitigating these attacks. Deep learning has been discovered to be an useful tactic for combating this issue, but there are still many open research questions and opportunities for improvement. As such, ongoing research and development in this field is crucial for staying ahead of the evolving threat landscape. Deep learning has evolved into a powerful technology for detecting cyber-attacks. We survey the most relevant deep learning models and datasets used for cyber-attack identification in this research. We will be discussing the various sorts of cyber based attacks that can be identified using deep learning, such as detection of network intruder, malware detection, as well as detection of anomaly in network in this survey. The most prominent deep learning architectures for cyber-attack detection, such as recurrent neural networks, deep belief neural network convolutional neural networks & autoencoders, are then examined. We also discuss the importance of using diverse and representative datasets for training and evaluating these models, and review some of the most widely used datasets in the field. At last, we talk about a few of the challenges and future headings within the field of profound learning for cyber-attack discovery. [ABSTRACT FROM AUTHOR]