# Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2106.09380 · HTML: https://mlsanity.com/paper/2106.09380/

## Key facts

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

## Built on by (most cited first, 30 of 41)

- ["Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice](https://mlsanity.com/paper/2212.14315/) (2022)
- [The Role of Machine Learning in Cybersecurity](https://mlsanity.com/paper/2206.09707/) (2022)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.06664/) (2023)
- [Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection](https://mlsanity.com/paper/2203.04234/) (2022)
- [SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2305.00550/) (2023)
- [Exploring Robust Architectures for Deep Artificial Neural Networks](https://mlsanity.com/paper/2106.15850/) (2021)
- [A Survey on Malware Detection with Graph Representation Learning](https://mlsanity.com/paper/2303.16004/) (2023)
- [Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification](https://mlsanity.com/paper/2301.13122/) (2023)
- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2203.04686/) (2022)
- [Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning](https://mlsanity.com/paper/2210.13660/) (2022)
- [TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2210.15700/) (2022)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors](https://mlsanity.com/paper/2212.05380/) (2022)
- [Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2](https://mlsanity.com/paper/2210.09028/) (2022)
- [Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS](https://mlsanity.com/paper/2111.12197/) (2021)
- [Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike](https://mlsanity.com/paper/2203.10166/) (2022)
- [Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.07003/) (2023)
- [PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis](https://mlsanity.com/paper/2404.10789/) (2024)
- [PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT](https://mlsanity.com/paper/2408.05667/) (2024)
- [Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection](https://mlsanity.com/paper/2205.15128/) (2022)
- [Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones](https://mlsanity.com/paper/2306.05494/) (2023)
- [Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification](https://mlsanity.com/paper/2212.14677/) (2022)
- [CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications](https://mlsanity.com/paper/2401.11126/) (2024)
- [A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS](https://mlsanity.com/paper/2409.07448/) (2024)
- [MLSMM: Machine Learning Security Maturity Model](https://mlsanity.com/paper/2306.16127/) (2023)
- [Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks](https://mlsanity.com/paper/2403.11830/) (2024)
- [A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection Systems](https://mlsanity.com/paper/2410.15597/) (2024)

## 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.