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Adversarial Machine Learning in Network Intrusion Detection Systems

arXiv 2004.11898 ↗First posted 23 Apr 2020cs.CR cs.LG cs.NEcited by 25

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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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 6; 2024: 5; 2025: 6; 2026: 4.

25 papers.

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  1. 2021Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Reviewcited by 8
  2. 2022ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systemscited by 8
  3. 2023Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection Systemcited by 8
  4. 2023Constrained Adversarial Learning for Automated Software Testing: a literature reviewcited by 6
  5. 2023A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection Systemcited by 6
  6. 2023Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generationcited by 5
  7. 2023Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectivescited by 5
  8. 2021Cybersecurity Anomaly Detection in Adversarial Environmentscited by 4
  9. 2024Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flowscited by 3
  10. 2024Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspectivecited by 3
  11. 2022A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection Systemcited by 2
  12. 2023A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attackscited by 2
13 more
  1. 2024Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layerscited by 2
  2. 2025Constrained Network Adversarial Attacks: Validity, Robustness, and Transferabilitycited by 1
  3. 2024Manipulating hidden-Markov-model inferences by corrupting batch datacited by 0
  4. 2024TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNNcited by 0
  5. 2025Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Reviewcited by 0
  6. 2025Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDScited by 0
  7. 2025VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learningcited by 0
  8. 2025Adapting Noise-Driven PUF and AI for Secure WBG ICS: A Proof-of-Concept Studycited by 0
  9. 2025Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprintingcited by 0
  10. 2026Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attackscited by 0
  11. 2026Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approachcited by 0
  12. 2026Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systemscited by 0
  13. 2026A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Trafficcited by 0