mlsanityData to 7 Oct 2026

Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems

arXiv 2303.07003 ↗First posted 13 Mar 2023cs.CR cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Nowadays, numerous applications incorporate machine learning (ML) algorithms due to their prominent achievements. However, many studies in the field of computer vision have shown that ML can be fooled by intentionally crafted instances, called adversarial examples. These adversarial examples take advantage of the intrinsic vulnerability of ML models. Recent research raises many concerns in the cybersecurity field. An increasing number of researchers are studying the feasibility of such attacks on security systems based on ML algorithms, such as Intrusion Detection Systems (IDS). The feasibility of such adversarial attacks would be influenced by various domain-specific constraints. This can potentially increase the difficulty of crafting adversarial examples. Despite the considerable amount of research that has been done in this area, much of it focuses on showing that it is possible to fool a model using features extracted from the raw data but does not address the practical side, i.e., the reverse transformation from theory to practice. For this reason, we propose a review browsing through various important papers to provide a comprehensive analysis. Our analysis highlights some challenges that have not been addressed in the reviewed papers.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 30 of its 62 references that are in the map. First posted on arXiv 13 Mar 2023.

Builds on

30 of 62 references.

References · 30

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  6. 2016Adversarial examples in the physical worldcited by 1,762
  7. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  8. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  9. 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 950
  10. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  11. 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
  12. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
18 more
  1. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  2. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  3. 2017Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networkscited by 484
  4. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
  5. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  6. 2017Detecting Adversarial Samples from Artifactscited by 244
  7. 2017EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examplescited by 154
  8. 2016Defensive Distillation is Not Robust to Adversarial Examplescited by 60
  9. 2018IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detectioncited by 41
  10. 2021Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systemscited by 39
  11. 2019Adversarial Examples in Modern Machine Learning: A Reviewcited by 36
  12. 2020Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectorscited by 25
  13. 2018Security and Privacy Issues in Deep Learningcited by 19
  14. 2019Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networkscited by 16
  15. 2022Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detectioncited by 13
  16. 2019Rallying Adversarial Techniques against Deep Learning for Network Securitycited by 11
  17. 2020Adversarial Network Traffic: Towards Evaluating the Robustness of Deep Learning-Based Network Traffic Classificationcited by 11
  18. 2022TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systemscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systemscited by 2
  2. 2026Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networkscited by 2
  3. 2026Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in IoT Networkscited by 0