# Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection

arXiv: https://arxiv.org/abs/2202.13611 · HTML: https://mlsanity.com/paper/2202.13611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [A Survey of Network-based Intrusion Detection Data Sets](https://mlsanity.com/paper/1903.02460/) (2019)
- [Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range](https://mlsanity.com/paper/1407.8038/) (2014)
- [A hybrid supervised/unsupervised machine learning approach to solar flare prediction](https://mlsanity.com/paper/1706.07103/) (2017)
- [Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape](https://mlsanity.com/paper/2012.11354/) (2020)

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

- [UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection](https://mlsanity.com/paper/2605.22621/) (2026)

## Abstract (arXiv)

In the last decades, researchers, practitioners and companies struggled in devising mechanisms to detect malicious activities originating security threats. Amongst the many solutions, network intrusion detection emerged as one of the most popular to analyze network traffic and detect ongoing intrusions based on rules or by means of Machine Learners (MLs), which process such traffic and learn a model to suspect intrusions. Supervised MLs are very effective in detecting known threats, but struggle in identifying zero-day attacks (unknown during learning phase), which instead can be detected through unsupervised MLs. Unfortunately, there are no definitive answers on the combined use of both approaches for network intrusion detection. In this paper we first expand the problem of zero-day attacks and motivate the need to combine supervised and unsupervised algorithms. We propose the adoption of meta-learning, in the form of a two-layer Stacker, to create a mixed approach that detects both known and unknown threats. Then we implement and empirically evaluate our Stacker through an experimental campaign that allows i) debating on meta-features crafted through unsupervised base-level learners, ii) electing the most promising supervised meta-level classifiers, and iii) benchmarking classification scores of the Stacker with respect to supervised and unsupervised classifiers. Last, we compare our solution with existing works from the recent literature. Overall, our Stacker reduces misclassifications with respect to (un)supervised ML algorithms in all the 7 public datasets we considered, and outperforms existing studies in 6 out of those 7 datasets. In particular, it turns out to be more effective in detecting zero-day attacks than supervised algorithms, limiting their main weakness but still maintaining adequate capabilities in detecting known attacks.