# Towards Explainable Meta-Learning for DDoS Detection

arXiv: https://arxiv.org/abs/2204.02255 · HTML: https://mlsanity.com/paper/2204.02255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [On the (Statistical) Detection of Adversarial Examples](https://mlsanity.com/paper/1702.06280/) (2017)
- [Abduction-Based Explanations for Machine Learning Models](https://mlsanity.com/paper/1811.10656/) (2018)
- [On The Reasons Behind Decisions](https://mlsanity.com/paper/2002.09284/) (2020)
- [An Adversarial Approach for Explainable AI in Intrusion Detection Systems](https://mlsanity.com/paper/1811.11705/) (2018)
- [Explainable Security](https://mlsanity.com/paper/1807.04178/) (2018)
- [On the Importance of Domain-specific Explanations in AI-based Cybersecurity Systems (Technical Report)](https://mlsanity.com/paper/2108.02006/) (2021)
- [On the Explanatory Power of Decision Trees](https://mlsanity.com/paper/2108.05266/) (2021)
- [Explaining Black-box Android Malware Detection](https://mlsanity.com/paper/1803.03544/) (2018)

## Abstract (arXiv)

The Internet is the most complex machine humankind has ever built, and how to defense it from intrusions is even more complex. With the ever increasing of new intrusions, intrusion detection task rely on Artificial Intelligence more and more. Interpretability and transparency of the machine learning model is the foundation of trust in AI-driven intrusion detection results. Current interpretation Artificial Intelligence technologies in intrusion detection are heuristic, which is neither accurate nor sufficient. This paper proposed a rigorous interpretable Artificial Intelligence driven intrusion detection approach, based on artificial immune system. Details of rigorous interpretation calculation process for a decision tree model is presented. Prime implicant explanation for benign traffic flow are given in detail as rule for negative selection of the cyber immune system. Experiments are carried out in real-life traffic.