mlsanityData to 14 Oct 2026

SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection

arXiv 2308.06819 ↗First posted 13 Aug 2023cs.CR cs.LG cs.NIcited by 10

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Abstract (arXiv)

Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in real ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their future experiments are adequate for a real communication network.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 58 of its 110 references that are in the map. First posted on arXiv 13 Aug 2023.

Builds on

58 of 110 references.

References · 58

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38 more
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50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 3; 2026: 1.

10 papers.

  1. 2024Adversarial Evasion Attack Efficiency against Large Language Modelscited by 4
  2. 2024An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detectioncited by 3
  3. 2024Reliable Feature Selection for Adversarially Robust Cyber-Attack Detectioncited by 3
  4. 2024Efficient Network Traffic Feature Sets for IoT Intrusion Detectioncited by 2
  5. 2024A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDScited by 1
  6. 2024Deep Generative Models for Subgraph Predictioncited by 0
  7. 2025SPATA: Systematic Pattern Analysis for Detailed and Transparent Data Cardscited by 0
  8. 2025Revisiting Network Traffic Analysis: Compatible network flows for ML modelscited by 0
  9. 2025Behavior-Aware and Generalizable Defense Against Black-Box Adversarial Attacks for ML-Based IDScited by 0
  10. 2026Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detectioncited by 0