# Adversarial Machine Learning in Network Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2004.11898 · HTML: https://mlsanity.com/paper/2004.11898/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 25 of 25)

- [ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems](https://mlsanity.com/paper/2201.02775/) (2022)
- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System](https://mlsanity.com/paper/2310.03334/) (2023)
- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)
- [Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation](https://mlsanity.com/paper/2305.11039/) (2023)
- [A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System](https://mlsanity.com/paper/2312.03245/) (2023)
- [Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives](https://mlsanity.com/paper/2307.01390/) (2023)
- [Cybersecurity Anomaly Detection in Adversarial Environments](https://mlsanity.com/paper/2105.06742/) (2021)
- [Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows](https://mlsanity.com/paper/2403.10550/) (2024)
- [Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective](https://mlsanity.com/paper/2404.10796/) (2024)
- [A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System](https://mlsanity.com/paper/2211.03933/) (2022)
- [A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks](https://mlsanity.com/paper/2308.00077/) (2023)
- [Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers](https://mlsanity.com/paper/2409.00667/) (2024)
- [Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability](https://mlsanity.com/paper/2505.01328/) (2025)
- [Manipulating hidden-Markov-model inferences by corrupting batch data](https://mlsanity.com/paper/2402.13287/) (2024)
- [TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN](https://mlsanity.com/paper/2409.12472/) (2024)
- [Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review](https://mlsanity.com/paper/2506.15506/) (2025)
- [Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS](https://mlsanity.com/paper/2506.20576/) (2025)
- [VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning](https://mlsanity.com/paper/2507.14625/) (2025)
- [Adapting Noise-Driven PUF and AI for Secure WBG ICS: A Proof-of-Concept Study](https://mlsanity.com/paper/2510.22283/) (2025)
- [Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting](https://mlsanity.com/paper/2511.06197/) (2025)
- [Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks](https://mlsanity.com/paper/2603.10413/) (2026)
- [Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach](https://mlsanity.com/paper/2605.08910/) (2026)
- [Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems](https://mlsanity.com/paper/2606.12075/) (2026)
- [A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic](https://mlsanity.com/paper/2607.17105/) (2026)

## Abstract (arXiv)

Adversarial examples are inputs to a machine learning system intentionally crafted by an attacker to fool the model into producing an incorrect output. These examples have achieved a great deal of success in several domains such as image recognition, speech recognition and spam detection. In this paper, we study the nature of the adversarial problem in Network Intrusion Detection Systems (NIDS). We focus on the attack perspective, which includes techniques to generate adversarial examples capable of evading a variety of machine learning models. More specifically, we explore the use of evolutionary computation (particle swarm optimization and genetic algorithm) and deep learning (generative adversarial networks) as tools for adversarial example generation. To assess the performance of these algorithms in evading a NIDS, we apply them to two publicly available data sets, namely the NSL-KDD and UNSW-NB15, and we contrast them to a baseline perturbation method: Monte Carlo simulation. The results show that our adversarial example generation techniques cause high misclassification rates in eleven different machine learning models, along with a voting classifier. Our work highlights the vulnerability of machine learning based NIDS in the face of adversarial perturbation.