# Behavior-Aware and Generalizable Defense Against Black-Box Adversarial Attacks for ML-Based IDS

arXiv: https://arxiv.org/abs/2512.13501 · HTML: https://mlsanity.com/paper/2512.13501/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 18 of 18)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Disentangling Adversarial Robustness and Generalization](https://mlsanity.com/paper/1812.00740/) (2018)
- [Adversarial Machine Learning -- Industry Perspectives](https://mlsanity.com/paper/2002.05646/) (2020)
- [DaST: Data-free Substitute Training for Adversarial Attacks](https://mlsanity.com/paper/2003.12703/) (2020)
- [Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2106.09380/) (2021)
- [Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.06664/) (2023)
- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [The LogBarrier adversarial attack: making effective use of decision boundary information](https://mlsanity.com/paper/1903.10396/) (2019)
- [Explainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors](https://mlsanity.com/paper/2401.10691/) (2024)
- [Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects](https://mlsanity.com/paper/2409.18736/) (2024)
- [Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator](https://mlsanity.com/paper/2109.05820/) (2021)
- [FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2210.04052/) (2022)

## Abstract (arXiv)

Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs using indirect feedback such as binary outputs or behavioral signals like response time and resource usage. While several defenses have been proposed, including input transformation, adversarial training, and surrogate detection, they often fall short in practice. Most are tailored to specific attack types, require internal model access, or rely on static mechanisms that fail to generalize across evolving attack strategies. Furthermore, defenses such as input transformation can degrade intrusion detection system performance, making them unsuitable for real time deployment. To address these limitations, we propose Adaptive Feature Poisoning, a lightweight and proactive defense mechanism designed specifically for realistic black box scenarios. Adaptive Feature Poisoning assumes that probing can occur silently and continuously, and introduces dynamic and context aware perturbations to selected traffic features, corrupting the attacker feedback loop without impacting detection capabilities. The method leverages traffic profiling, change point detection, and adaptive scaling to selectively perturb features that an attacker is likely exploiting, based on observed deviations. We evaluate Adaptive Feature Poisoning against multiple realistic adversarial attack strategies, including silent probing, transferability based attacks, and decision boundary based attacks. The results demonstrate its ability to confuse attackers, degrade attack effectiveness, and preserve detection performance. By offering a generalizable, attack agnostic, and undetectable defense, Adaptive Feature Poisoning represents a significant step toward practical and robust adversarial resilience in machine learning based intrusion detection systems.