# Artificial Neural Network for Cybersecurity: A Comprehensive Review

arXiv: https://arxiv.org/abs/2107.01185 · HTML: https://mlsanity.com/paper/2107.01185/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 125 references that are in the map.
- First posted on arXiv 20 Jun 2021.

## Builds on (most cited first, 24 of 24)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning](https://mlsanity.com/paper/1709.02656/) (2017)
- [Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features](https://mlsanity.com/paper/1508.03096/) (2015)
- [Adversarial Perturbations Against Deep Neural Networks for Malware Classification](https://mlsanity.com/paper/1606.04435/) (2016)
- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys](https://mlsanity.com/paper/1702.08568/) (2017)
- [DeepDGA: Adversarially-Tuned Domain Generation and Detection](https://mlsanity.com/paper/1610.01969/) (2016)
- [Predicting Domain Generation Algorithms with Long Short-Term Memory Networks](https://mlsanity.com/paper/1611.00791/) (2016)
- [LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems](https://mlsanity.com/paper/1611.01726/) (2016)
- [A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data](https://mlsanity.com/paper/1709.03082/) (2017)
- [DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification](https://mlsanity.com/paper/1711.08336/) (2017)
- [Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review](https://mlsanity.com/paper/2006.01767/) (2020)
- [Threats and Countermeasures of Cyber Security in Direct and Remote Vehicle Communication Systems](https://mlsanity.com/paper/2006.08723/) (2020)
- [Automatic Detection of Malware-Generated Domains with Recurrent Neural Models](https://mlsanity.com/paper/1709.07102/) (2017)
- [Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach](https://mlsanity.com/paper/1712.05914/) (2017)
- [Review on the Security Threats of Internet of Things](https://mlsanity.com/paper/2101.05614/) (2021)
- [A multi-task learning model for malware classification with useful file access pattern from API call sequence](https://mlsanity.com/paper/1610.05945/) (2016)
- [Deep Learning for Secure Mobile Edge Computing](https://mlsanity.com/paper/1709.08025/) (2017)
- [Deep Learning Based Cryptographic Primitive Classification](https://mlsanity.com/paper/1709.08385/) (2017)

## Built on by (most cited first, 4 of 4)

- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey](https://mlsanity.com/paper/2401.05896/) (2024)
- [Viskositas: Viscosity Prediction of Multicomponent Chemical Systems](https://mlsanity.com/paper/2208.01440/) (2022)
- [Payload-Aware Intrusion Detection with CMAE and Large Language Models](https://mlsanity.com/paper/2503.20798/) (2025)

## Abstract (arXiv)

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.