Vol.:(0123456789) SN Computer Science (2021) 2:154 https://doi.org/10.1007/s42979-021-00535-6 SN Computer Science SURVEY ARTICLE Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective Iqbal H. Sarker 1,2 Received: 19 November 2020 / Accepted: 19 February 2021 © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature 2021 Abstract Deep learning, which is originated from an artifcial neural network (ANN), is one of the major technologies of today’s smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as multi-layer perceptron, convolutional neural network, recurrent neural network or long short-term memory, self-organizing map, auto- encoder, restricted Boltzmann machine, deep belief networks, generative adversarial network, deep transfer learning, as well as deep reinforcement learning, or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today’s diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identifcation of malware or botnets, phishing, predicting cyberat- tacks, e.g. denial of service, fraud detection or cyberanomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the feld. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view. Keywords Cybersecurity · Deep learning · Artifcial neural network · Artifcial intelligence · Cyberattacks · Cybersecurity analytics · Cyber threat intelligence Introduction Due to the increasing popularity of internet-of-things (IoT) [1], and today’s dependency on digitalization, various security incidents or attacks have grown rapidly in recent years. Malicious activities, malware or ransomware attack [2], zero-day attack [3], cryptographic attack, unauthor- ized access [4], denial of service (DoS) [4], data breaches [5], phishing or social engineering [6], or various attacks on IoT devices etc. are common nowadays. These types of security incidents or cybercrime can afect organizations and individuals, cause disruptions, as well as devastating fnancial losses. For example, a data breach costs 8.19 mil- lion USD for the United States [7] according to the IBM report, and the total annual cost of cybercrime to the global economy is 400 billion USD [8]. Cybercrimes are growing at an exponential rate that brings an alarming message for the cybersecurity professionals and researchers [9]. Thus, the security management tools having the capability of detecting and preventing such incidents in a timely and intelligent way is urgently needed, on which the overall national security of the business, government, and individual citizens of a country depends. Typically, cybersecurity is characterized as a collection of technologies and processes designed to protect computers, networks, programs, and data against malicious activities, attacks, harm, or unauthorized access [10]. According to today’s numerous needs, conventional well-known secu- rity solutions such as antivirus, frewalls, user authentica- tion, encryption etc. may not be efective [11–14]. The key issue with these systems is that they are normally operated by a few security analysts, where data management is car- ried out in an ad hoc manner and can, therefore, not work This article is part of the topical collection “Deep learning approaches for data analysis: A practical perspective” guest edited by D. Jude Hemanth, Lipo Wang and Anastasia Angelopoulou. * Iqbal H. Sarker msarker@swin.edu.au 1 Swinburne University of Technology, Melbourne, VIC 3122, Australia 2 Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh Content courtesy of Springer Nature, terms of use apply. Rights reserved.