Abstract
Deep learning, which is originated from an artificial 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, identification of malware or botnets, phishing, predicting cyberattacks, 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 field. 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.
Highlights
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
Deep learning (DL) is considered as a part of machine learning (ML) as well as artificial intelligence (AI), which is originated from an artificial neural network (ANN) and one of the major technologies of the Fourth Industrial Revolution (Industry 4.0) [9] [17]
The data-driven model based on ANN and DL methods is usually based on data availability [20]
Summary
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. We take into account ten popular neural network and deep learning techniques including supervised, semi-supervised, unsupervised, and reinforcement learning in the context of cybersecurity These are (i) multi-layer perceptron (MLP), (ii) convolutional neural network (CNN or ConvNet), (iii) recurrent neural network (RNN) or long short-term memory (LSTM), (iv) self-organizing map (SOM), (v) auto-encoder (AE), (vi) restricted Boltzmann machine (RBM), (vii) deep belief networks (DBN), (viii) generative adversarial network (GAN), (ix) deep transfer learning (DTL or deep TL), and (x) deep reinforcement learning (DRL or deep RL).
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