Deep Learning Approaches in Cyber Security-A Comprehensive Survey
V. Jayabharathi, Sutheesh Sukumaran · Zenodo (CERN European Organization for Nuclear Research) · 2022
Recent years have seen the successful application of deep learning techniques, an enhanced model of conventional machine learning, in a variety of fields, including banking, entertainment, coordinating, health care, and cyber security. The study concentrated on a thorough examination of deep learning techniques in cyber security. Adversarial attacks have emerged as a more significant security threat to many deep learning applications than machine learning in the real world as deep learning techniques have become the core components for many security-critical applications such as identity recognition cameras, malware detection software, intrusion detection, spam detection, and selfdriving cars. Through a review of the literature and consideration of the important research topics, this paper gives a thorough study on the Deep Learning process, supervised, and unsupervised approaches. The survey also discusses important DL architectures used in cyber security applications.