mlsanityData to 14 Oct 2026

Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems

arXiv 2106.09380 ↗First posted 17 Jun 2021cs.CRcited by 41

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Abstract (arXiv)

The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that are inappropriate for realistic cybersecurity scenarios because they consider opponents with complete knowledge about the cyber detector or that can freely interact with the target systems. By focusing on Network Intrusion Detection Systems based on machine learning, we identify and model the real capabilities and circumstances required by attackers to carry out feasible and successful adversarial attacks. We then apply our model to several adversarial attacks proposed in literature and highlight the limits and merits that can result in actual adversarial attacks. The contributions of this paper can help hardening defensive systems by letting cyber defenders address the most critical and real issues, and can benefit researchers by allowing them to devise novel forms of adversarial attacks based on realistic threat models.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jun 2021.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 12; 2023: 9; 2024: 6; 2025: 2; 2026: 9.

41 papers.

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  1. 2022"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practicecited by 56
  2. 2022The Role of Machine Learning in Cybersecuritycited by 22
  3. 2023Deep transfer learning for intrusion detection in industrial control networks: A comprehensive reviewcited by 21
  4. 2023Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systemscited by 14
  5. 2022Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detectioncited by 13
  6. 2023SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detectioncited by 13
  7. 2021Exploring Robust Architectures for Deep Artificial Neural Networkscited by 11
  8. 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
  9. 2023Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classificationcited by 10
  10. 2023SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detectioncited by 10
  11. 2021Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Reviewcited by 8
  12. 2022The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systemscited by 8
29 more
  1. 2022Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learningcited by 7
  2. 2022TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systemscited by 7
  3. 2022Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examplescited by 5
  4. 2022Mitigating Adversarial Gray-Box Attacks Against Phishing Detectorscited by 5
  5. 2022Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2cited by 4
  6. 2021Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDScited by 3
  7. 2022Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alikecited by 3
  8. 2023Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systemscited by 3
  9. 2024PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysiscited by 3
  10. 2024PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERTcited by 3
  11. 2022Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detectioncited by 2
  12. 2023Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Onescited by 2
  13. 2022Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identificationcited by 1
  14. 2024CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applicationscited by 1
  15. 2024A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDScited by 1
  16. 2023MLSMM: Machine Learning Security Maturity Modelcited by 0
  17. 2024Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networkscited by 0
  18. 2024A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection Systemscited by 0
  19. 2025Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDScited by 0
  20. 2025Behavior-Aware and Generalizable Defense Against Black-Box Adversarial Attacks for ML-Based IDScited by 0
  21. 2026Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in IoT Networkscited by 0
  22. 2026AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detectioncited by 0
  23. 2026Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systemscited by 0
  24. 2026Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifierscited by 0
  25. 2026Adaptive Intrusion Detection System using Transformer-Based Neural Networks and Continual Learning Approach with Adversarial Investigationcited by 0
  26. 2026Concept drift mitigation through community and spectral graph analysis for the detection of cyberattacks in network trafficcited by 0
  27. 2026Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attackscited by 0
  28. 2026Robust Conformal Intrusion Detection via Traffic-Aware Calibration and Attack-Orbit Invariancecited by 0
  29. 2026Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detectioncited by 0