Artificial Neural Network for Cybersecurity: A Comprehensive Review

Abstract

Cybersecurity is a very emerging field that protects systems, networks, and data from digital attacks. With the increase in the scale of the Internet and the evolution of cyber attacks, developing novel cybersecurity tools has become important, particularly for Internet of things (IoT) networks. This paper provides a systematic review of the application of deep learning (DL) approaches for cybersecurity. This paper provides a short description of DL methods which is used in cybersecurity, including deep belief networks, generative adversarial networks, recurrent neural networks, and others. Next, we illustrate the differences between shallow learning and DL. Moreover, a discussion is provided on the currently prevailing cyber-attacks in IoT and other networks, and the effectiveness of DL methods to manage these attacks. Besides, this paper describes studies that highlight the DL technique, cybersecurity applications, and the source of datasets. Next, a discussion is provided on the feasibility of DL systems for malware detection and classification, intrusion detection, and other frequent cyber-attacks, including identifying file type, spam, and network traffic. Our review indicates that high classification accuracy of 99.72% is obtained by restricted Boltzmann machine (RBM) when applied to a custom dataset, while long short-term memory (LSTM) achieves an accuracy of 99.80% for KDD Cup 99 dataset. Finally, this article discusses the importance of cybersecurity for reliable and practicable IoT-driven healthcare systems.

14 Pages, 8 Figures

References in corpus (12)

• Sequence to Sequence Learning with Neural Networks
• DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
• Predicting Domain Generation Algorithms with Long Short-Term Memory Networks
• eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys
• LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems
• Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review
• Automatic Detection of Malware-Generated Domains with Recurrent Neural Models
• Review on the Security Threats of Internet of Things
• Deep Learning for Secure Mobile Edge Computing
• A multi-task learning model for malware classification with useful file access pattern from API call sequence
• Deep Learning Based Cryptographic Primitive Classification
• Threats and Countermeasures of Cyber Security in Direct and Remote Vehicle Communication Systems