IDS means intrusion detection systems, and it can be said that ID S is a tool with which an organization, as part of its defense from cyberinvasion, operates on networks and systems. As of this paper, the researcher seeks to give a clear understanding of IDS technologies with special focus on deep learning (DL) solutions. The conversion process of sig-based IDS to newer types of anomaly detection is provided, and the problems presented with the previous methodologies are also explained, along with corresponding recommendations for future enhancement of IDS. CNNs, RNNs, GAN, and other DL models are described, and regarding their use in IDS, their ability to detect subtle patterns and abnormal behavior in traffic and syslog are described. Also, we present an overview of related problems in DL-based IDS and their solutions such as imbalanced dataset, feature selection, computational complexity, and future work. With the help of this survey, the understanding of the modern IDS that can use DL approaches is given, which underscore the need for developing IDS and its interaction with other higher technologies to enhance an organization’s security in the contemporary digital setting.