# A Case Study on Using Deep Learning for Network Intrusion Detection

arXiv: https://arxiv.org/abs/1910.02203 · HTML: https://mlsanity.com/paper/1910.02203/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges](https://mlsanity.com/paper/2506.02032/) (2025)
- [DoS and DDoS Mitigation Using Variational Autoencoders](https://mlsanity.com/paper/2105.06899/) (2021)
- [Scalable Microservice Forensics and Stability Assessment Using Variational Autoencoders](https://mlsanity.com/paper/2104.13193/) (2021)

## Abstract (arXiv)

Deep Learning has been very successful in many application domains. However, its usefulness in the context of network intrusion detection has not been systematically investigated. In this paper, we report a case study on using deep learning for both supervised network intrusion detection and unsupervised network anomaly detection. We show that Deep Neural Networks (DNNs) can outperform other machine learning based intrusion detection systems, while being robust in the presence of dynamic IP addresses. We also show that Autoencoders can be effective for network anomaly detection.