mlsanityData to 7 Oct 2026

Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review

arXiv 2112.03315 ↗First posted 6 Dec 2021cs.CR cs.LG cs.NEcited by 8

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Abstract (arXiv)

Due to their massive success in various domains, deep learning techniques are increasingly used to design network intrusion detection solutions that detect and mitigate unknown and known attacks with high accuracy detection rates and minimal feature engineering. However, it has been found that deep learning models are vulnerable to data instances that can mislead the model to make incorrect classification decisions so-called (adversarial examples). Such vulnerability allows attackers to target NIDSs by adding small crafty perturbations to the malicious traffic to evade detection and disrupt the system's critical functionalities. The problem of deep adversarial learning has been extensively studied in the computer vision domain; however, it is still an area of open research in network security applications. Therefore, this survey explores the researches that employ different aspects of adversarial machine learning in the area of network intrusion detection in order to provide directions for potential solutions. First, the surveyed studies are categorized based on their contribution to generating adversarial examples, evaluating the robustness of ML-based NIDs towards adversarial examples, and defending these models against such attacks. Second, we highlight the characteristics identified in the surveyed research. Furthermore, we discuss the applicability of the existing generic adversarial attacks for the NIDS domain, the feasibility of launching the proposed attacks in real-world scenarios, and the limitations of the existing mitigation solutions.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 28 of its 109 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

28 of 109 references.

References · 28

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  2. 2013Intriguing properties of neural networkscited by 5,114
  3. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  4. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  5. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  6. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  7. 2018Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detectioncited by 121
  8. 2018Deep Learning in Mobile and Wireless Networking: A Surveycited by 117
  9. 2018Adversarially Learned Anomaly Detectioncited by 115
  10. 2018Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Datasetcited by 98
  11. 2018IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detectioncited by 41
  12. 2021Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systemscited by 39
16 more
  1. 2020Adversarial Machine Learning in Network Intrusion Detection Systemscited by 25
  2. 2018Adversarial Attacks on Variational Autoencoderscited by 19
  3. 2019Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networkscited by 16
  4. 2019Rallying Adversarial Techniques against Deep Learning for Network Securitycited by 11
  5. 2021TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attackcited by 11
  6. 2019Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimizationcited by 10
  7. 2019The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Surveycited by 9
  8. 2020Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSscited by 9
  9. 2020Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systemscited by 7
  10. 2021Adversarial Training for Deep Learning-based Intrusion Detection Systemscited by 6
  11. 2016A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN)cited by 5
  12. 2019Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Frameworkcited by 5
  13. 2020NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusioncited by 5
  14. 2021Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attackscited by 4
  15. 2021Packet-Level Adversarial Network Traffic Crafting using Sequence Generative Adversarial Networkscited by 3
  16. 2019The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?cited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 2; 2026: 3.

8 papers.

  1. 2024Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspectivecited by 3
  2. 2023Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Onescited by 2
  3. 2025A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systemscited by 2
  4. 2025Detecting and Mitigating DDoS Attacks with AI: A Surveycited by 1
  5. 2023Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detectioncited by 0
  6. 2026Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attackscited by 0
  7. 2026A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Trafficcited by 0
  8. 2026Diff-DDoS: Realistic Cyber-Physical Attack Synthesis and Robust Detection for 5G-Enabled CPS Using Tabular Diffusion Modelscited by 0