mlsanityData to 7 Oct 2026

Artificial Neural Network for Cybersecurity: A Comprehensive Review

arXiv 2107.01185 ↗First posted 20 Jun 2021cs.CR cs.LG eess.SPcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 125 references that are in the map. First posted on arXiv 20 Jun 2021.

Builds on

24 of 125 references.

References · 24

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12 more
  1. 2016Predicting Domain Generation Algorithms with Long Short-Term Memory Networkscited by 13
  2. 2016LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systemscited by 9
  3. 2017A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Datacited by 8
  4. 2017DeepSign: Deep Learning for Automatic Malware Signature Generation and Classificationcited by 8
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  7. 2017Automatic Detection of Malware-Generated Domains with Recurrent Neural Modelscited by 3
  8. 2017Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approachcited by 3
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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 1.

4 papers.

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