# A Compendium on Network and Host based Intrusion Detection Systems

arXiv: https://arxiv.org/abs/1904.03491 · HTML: https://mlsanity.com/paper/1904.03491/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN](https://mlsanity.com/paper/2607.02981/) (2026)

## Abstract (arXiv)

The techniques of deep learning have become the state of the art methodology for executing complicated tasks from various domains of computer vision, natural language processing, and several other areas. Due to its rapid development and promising benchmarks in those fields, researchers started experimenting with this technique to perform in the area of, especially in intrusion detection related tasks. Deep learning is a subset and a natural extension of classical Machine learning and an evolved model of neural networks. This paper contemplates and discusses all the methodologies related to the leading edge Deep learning and Neural network models purposing to the arena of Intrusion Detection Systems.