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

arXiv: https://arxiv.org/abs/2112.03315 · HTML: https://mlsanity.com/paper/2112.03315/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 109 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 28 of 28)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection](https://mlsanity.com/paper/1809.02077/) (2018)
- [Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2106.09380/) (2021)
- [Adversarial Machine Learning in Network Intrusion Detection Systems](https://mlsanity.com/paper/2004.11898/) (2020)
- [Adversarial Attacks on Variational Autoencoders](https://mlsanity.com/paper/1806.04646/) (2018)
- [Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/1905.05137/) (2019)
- [TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack](https://mlsanity.com/paper/2103.06297/) (2021)
- [Rallying Adversarial Techniques against Deep Learning for Network Security](https://mlsanity.com/paper/1903.11688/) (2019)
- [Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization](https://mlsanity.com/paper/1910.14107/) (2019)
- [The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey](https://mlsanity.com/paper/1911.02621/) (2019)
- [Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs](https://mlsanity.com/paper/2007.04472/) (2020)
- [Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems](https://mlsanity.com/paper/2008.03677/) (2020)
- [Adversarial Training for Deep Learning-based Intrusion Detection Systems](https://mlsanity.com/paper/2104.09852/) (2021)
- [A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN)](https://mlsanity.com/paper/1611.07400/) (2016)
- [Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework](https://mlsanity.com/paper/1901.07949/) (2019)
- [NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion](https://mlsanity.com/paper/2002.08527/) (2020)
- [Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks](https://mlsanity.com/paper/2105.14564/) (2021)
- [Packet-Level Adversarial Network Traffic Crafting using Sequence Generative Adversarial Networks](https://mlsanity.com/paper/2103.04794/) (2021)
- [The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?](https://mlsanity.com/paper/1906.00679/) (2019)

## Built on by (most cited first, 8 of 8)

- [Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective](https://mlsanity.com/paper/2404.10796/) (2024)
- [Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones](https://mlsanity.com/paper/2306.05494/) (2023)
- [A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems](https://mlsanity.com/paper/2502.15561/) (2025)
- [Detecting and Mitigating DDoS Attacks with AI: A Survey](https://mlsanity.com/paper/2503.17867/) (2025)
- [Diff-DDoS: Realistic Cyber-Physical Attack Synthesis and Robust Detection for 5G-Enabled CPS Using Tabular Diffusion Models](https://mlsanity.com/paper/2608.17796/) (2026)
- [Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection](https://mlsanity.com/paper/2302.00537/) (2023)
- [Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks](https://mlsanity.com/paper/2603.10413/) (2026)
- [A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic](https://mlsanity.com/paper/2607.17105/) (2026)

## 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.