# Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection

arXiv: https://arxiv.org/abs/2302.01759 · HTML: https://mlsanity.com/paper/2302.01759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [A Survey of Network-based Intrusion Detection Data Sets](https://mlsanity.com/paper/1903.02460/) (2019)
- [Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series](https://mlsanity.com/paper/1602.07109/) (2016)
- [Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle](https://mlsanity.com/paper/1906.11976/) (2019)

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

- [A Graph Prompt Fine-Tuning Method for WSN Spatio-Temporal Correlation Anomaly Detection](https://mlsanity.com/paper/2601.12745/) (2026)

## Abstract (arXiv)

Network anomaly detection is a very relevant research area nowadays, especially due to its multiple applications in the field of network security. The boost of new models based on variational autoencoders and generative adversarial networks has motivated a reevaluation of traditional techniques for anomaly detection. It is, however, essential to be able to understand these new models from the perspective of the experience attained from years of evaluating network security data for anomaly detection. In this paper, we revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view, and contribute a mathematical model that relates them. Specifically, we start with the probabilistic PCA model and explain its connection to the Multivariate Statistical Network Monitoring (MSNM) framework. MSNM was recently successfully proposed as a means of incorporating industrial process anomaly detection experience into the field of networking. We have evaluated the mathematical model using two different datasets. The first, a synthetic dataset created to better understand the analysis proposed, and the second, UGR'16, is a specifically designed real-traffic dataset for network security anomaly detection. We have drawn conclusions that we consider to be useful when applying generative models to network security detection.