Application of deep learning to cybersecurity: A survey

Abstract

Cutting edge Deep Learning (DL) techniques have been widely applied to areas like image processing and speech recognition so far. Likewise, some DL work has been done in the area of cybersecurity. In this survey, we focus on recent DL approaches that have been proposed in the area of cybersecurity, namely intrusion detection, malware detection, phishing/spam detection, and website defacement detection. First, preliminary definitions of popular DL models and algorithms are described. Then, a general DL framework for cybersecurity applications is proposed and explained based on the four major modules it consists of. Afterward, related papers are summarized and analyzed with regard to the focus area, methodology, model applicability, and feature granularity. Finally, concluding remarks and future work are discussed including the possible research topics that can be taken into consideration to enhance various cybersecurity applications using DL models.