# Robustness of ML-Enhanced IDS to Stealthy Adversaries

arXiv: https://arxiv.org/abs/2104.10742 · HTML: https://mlsanity.com/paper/2104.10742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 21 Apr 2021.

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

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection](https://mlsanity.com/paper/1903.06661/) (2019)
- [Defining a Metric Space of Host Logs and Operational Use Cases](https://mlsanity.com/paper/1811.00591/) (2018)

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

- [End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings](https://mlsanity.com/paper/2108.12276/) (2021)

## Abstract (arXiv)

Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have demonstrated the capacity to efficiently build a prototype of "normal" cyber behaviors in order to detect cyber threats' activity with greater accuracy than traditional rule-based IDS. Because these are largely black boxes, their acceptance requires proof of robustness to stealthy adversaries. Since it is impossible to build a baseline from activity completely clean of that of malicious cyber actors (outside of controlled experiments), the training data for deployed models will be poisoned with examples of activity that analysts would want to be alerted about. We train an autoencoder-based anomaly detection system on network activity with various proportions of malicious activity mixed in and demonstrate that they are robust to this sort of poisoning.