Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

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, autoencoder, 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.

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

“…In addition, grid-based clustering methods, such as STING [134], CLIQUE [2], etc. ; model-based clustering such as neural network learning [141], GMM [94], SOM [18,104], etc. ; constrainedbased methods such as COP K-means [131], CMWK-Means [25], etc.…”

“…In addition to the most popular deep learning methods mentioned above, several other deep learning approaches [104] exist in the field for various purposes. The self-organizing map (SOM) [58], for example, uses unsupervised learning to represent high-dimensional data as a 2D grid map, reducing dimensionality.…”

“…These deep learning methods can perform well, particularly, when learning from large-scale datasets [105,140]. In our previous article Sarker et al [104], we have summarized a brief discussion of various artificial neural networks (ANN) and deep learning (DL) models mentioned above, which can be used in a variety of data science and analytics tasks.…”

“…For example, using the relevant data, data science methods can be used for traffic prediction in smart cities, to estimate the total usage of energy of the citizens for a particular period. Deep learning-based models in data science can be built based on a large scale of IoT datasets [7,104]. Overall, data science and analytics approaches can aid modeling in a variety of IoT and smart city services, including smart governance, smart homes, education, connectivity, transportation, business, agriculture, health care, and industry, and many others.…”

“…-In several important application areas such as autonomous cars, criminal justice, health care, recruitment, housing, management of the human resource, public safety, where decisions made by models, or AI agents, have a direct effect on human lives. As a result, there is growing concerned about whether these decisions can be trusted, to be right, reasonable, ethical, personalized, accurate, robust, and secure, particularly in the context of adversarial attacks [104]. If we can explain the result in a meaningful way, then the model can be better trusted by the end-user.…”