Figure 1. ”Cyber security” and ”Deep learning” popularity scores in a range of 0 (min) to 100 (max) through time, with the x-axis representing timestamp information and the y-axis representing the associated popularity score.
The popularity trend in Figure 1 is based on Google Trends data collected over the previous five years. In this research, we consider several standard neural networks and deep learning approaches in the context of cybersecurity, including supervised, semi-supervised, unsupervised, and reinforcement learning. These include I multilayer perceptron (MLP), convolutional neural network (CNN or ConvNet), (ii) 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) (DRL or deep RL). These deep neural network learning techniques and their ensembles and hybrid approaches can intelligently solve various cybersecurity problems, including intrusion detection, malware or botnet identification, phishing, and predicting cyber-attacks such as DoS fraud detection and cyber-anomalies [7]. Construct security models because they are more accurate, especially when learning from massive security datasets. This paper’s contribution may be summarised as follows: