# Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness

arXiv: https://arxiv.org/abs/2403.10461 · HTML: https://mlsanity.com/paper/2403.10461/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Detecting Adversarial Samples from Artifacts](https://mlsanity.com/paper/1703.00410/) (2017)
- [SoK: The Impact of Unlabelled Data in Cyberthreat Detection](https://mlsanity.com/paper/2205.08944/) (2022)
- [Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization](https://mlsanity.com/paper/1910.14107/) (2019)
- [The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey](https://mlsanity.com/paper/1911.02621/) (2019)
- [Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs](https://mlsanity.com/paper/2007.04472/) (2020)

## Abstract (arXiv)

Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in network/cybersecurity domains is challenging and costly. Therefore, this letter introduces Adaptive Continuous Adversarial Training (ACAT), a method that integrates adversarial training samples into the model during continuous learning sessions using real-world detected adversarial data. Experimental results with a SPAM detection dataset demonstrate that ACAT reduces the time required for adversarial sample detection compared to traditional processes. Moreover, the accuracy of the under-attack ML-based SPAM filter increased from 69% to over 88% after just three retraining sessions.