Abstract
Deep Learning (DL), a novel form of machine learning (ML) is gaining
much research interest due to its successful application in many
classical artificial intelligence (AI) tasks as compared to classical ML
algorithms (CMLAs). Recently, DL architectures are being innovatively
modelled for diverse applications in the area of cyber security. The
literature is now growing with DL architectures and their variations for
exploring different innovative DL models and prototypes that can be
tailored to suit specific cyber security applications. However, there is
a gap in literature for a comprehensive survey reporting on such
research studies. Many of the survey-based research have a focus on
specific DL architectures and certain types of malicious attacks within
a limited cyber security problem scenario of the past and lack
futuristic review. This paper aims at providing a well-rounded and
thorough survey of the past, present, and future DL architectures
including next-generation cyber security scenarios related to
intelligent automation, Internet of Things (IoT), Big Data (BD),
Blockchain, cloud and edge technologies.
This paper presents a tutorial-style comprehensive review of the
state-of-the-art DL architectures for diverse applications in cyber
security by comparing and analysing the contributions and challenges
from various recent research papers. Firstly, the uniqueness of the
survey is in reporting the use of DL architectures for an extensive set
of cybercrime detection approaches such as intrusion detection, malware
and botnet detection, spam and phishing detection, network traffic
analysis, binary analysis, insider threat detection, CAPTCHA analysis,
and steganography. Secondly, the survey covers key DL architectures in
cyber security application domains such as cryptography, cloud security,
biometric security, IoT and edge computing. Thirdly, the need for DL
based research is discussed for the next generation cyber security
applications in cyber physical systems (CPS) that leverage on BD
analytics, natural language processing (NLP), signal and image
processing and blockchain technology for smart cities and Industry 4.0
of the future. Finally, a critical discussion on open challenges and new
proposed DL architecture contributes towards future research directions.