# A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System

arXiv: https://arxiv.org/abs/2111.10280 · HTML: https://mlsanity.com/paper/2111.10280/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Explainability in Human-Agent Systems](https://mlsanity.com/paper/1904.08123/) (2019)
- [Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System](https://mlsanity.com/paper/1710.04843/) (2017)
- [A Comparative Review of Microservices and Monolithic Architectures](https://mlsanity.com/paper/1905.07997/) (2019)

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

- [Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2207.06236/) (2022)
- [Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods](https://mlsanity.com/paper/2212.13991/) (2022)
- [Signed Latent Factors for Spamming Activity Detection](https://mlsanity.com/paper/2209.13814/) (2022)

## Abstract (arXiv)

Cybersecurity has been a concern for quite a while now. In the latest years, cyberattacks have been increasing in size and complexity, fueled by significant advances in technology. Nowadays, there is an unavoidable necessity of protecting systems and data crucial for business continuity. Hence, many intrusion detection systems have been created in an attempt to mitigate these threats and contribute to a timelier detection. This work proposes an interpretable and explainable hybrid intrusion detection system, which makes use of artificial intelligence methods to achieve better and more long-lasting security. The system combines experts' written rules and dynamic knowledge continuously generated by a decision tree algorithm as new shreds of evidence emerge from network activity.