Deep Learning Methods applied to Intrusion Detection: Survey, Taxonomy and Challenges

Because of the popularity of the Internet of Things (IoT), the rapid expansion of computer networks, and the vast number of important applications, cyber security has lately garnered a lot of attention in today's security issues. As a result, identifying different cyber-attacks or abnormalities in a network, as well as constructing an efficient intrusion detection system that plays a key part in today's security, is becoming increasingly critical. Such a data-driven intelligent intrusion detection system may be built using artificial intelligence, particularly machine learning techniques. This survey provides a thorough review of Machine Learning (ML) techniques for cybersecurity intrusion detection systems, with an emphasis on new Deep Learning-based approaches (DL). The study examines current techniques in terms of intrusion detection processes, performance outcomes, and limits, as well as whether or not they use benchmark datasets to provide a fair assessment. In addition, a thorough examination of cybersecurity benchmark datasets is provided. This article aims to offer a roadmap for readers interested in learning more about the potential of deep learning techniques for cybersecurity and intrusion detection systems, as well as a thorough examination of the benchmark datasets used to train DL models in the literature.