# Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification

arXiv: https://arxiv.org/abs/2301.13122 · HTML: https://mlsanity.com/paper/2301.13122/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 52 references that are in the map.
- First posted on arXiv 30 Jan 2023.

## Builds on (most cited first, 21 of 21)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Backdoor Learning: A Survey](https://mlsanity.com/paper/2007.08745/) (2020)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [Understanding and Improving Fast Adversarial Training](https://mlsanity.com/paper/2007.02617/) (2020)
- [Disentangling Adversarial Robustness and Generalization](https://mlsanity.com/paper/1812.00740/) (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)
- [Evasion and Hardening of Tree Ensemble Classifiers](https://mlsanity.com/paper/1509.07892/) (2015)
- [Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2106.09380/) (2021)
- [Robust Decision Trees Against Adversarial Examples](https://mlsanity.com/paper/1902.10660/) (2019)
- [Security Evaluation of Pattern Classifiers under Attack](https://mlsanity.com/paper/1709.00609/) (2017)
- [Efficient Training of Robust Decision Trees Against Adversarial Examples](https://mlsanity.com/paper/2012.10438/) (2020)
- [Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things](https://mlsanity.com/paper/1911.05771/) (2019)
- [Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection](https://mlsanity.com/paper/2203.04234/) (2022)
- [Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures](https://mlsanity.com/paper/1910.13312/) (2019)
- [Hardening Random Forest Cyber Detectors Against Adversarial Attacks](https://mlsanity.com/paper/1912.03790/) (2019)
- [Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems](https://mlsanity.com/paper/2004.05005/) (2020)
- [A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection](https://mlsanity.com/paper/2111.13149/) (2021)
- [Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT](https://mlsanity.com/paper/2104.12426/) (2021)

## Built on by (most cited first, 10 of 10)

- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)
- [An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection](https://mlsanity.com/paper/2402.16912/) (2024)
- [Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection](https://mlsanity.com/paper/2404.04188/) (2024)
- [Efficient Network Traffic Feature Sets for IoT Intrusion Detection](https://mlsanity.com/paper/2406.08042/) (2024)
- [Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks](https://mlsanity.com/paper/2607.00553/) (2026)
- [IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection](https://mlsanity.com/paper/2401.01343/) (2023)
- [A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS](https://mlsanity.com/paper/2409.07448/) (2024)
- [Flow Exporter Impact on Intelligent Intrusion Detection Systems](https://mlsanity.com/paper/2412.14021/) (2024)
- [Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection](https://mlsanity.com/paper/2303.13649/) (2023)

## Abstract (arXiv)

The Internet of Things (IoT) faces tremendous security challenges. Machine learning models can be used to tackle the growing number of cyber-attack variations targeting IoT systems, but the increasing threat posed by adversarial attacks restates the need for reliable defense strategies. This work describes the types of constraints required for a realistic adversarial cyber-attack example and proposes a methodology for a trustworthy adversarial robustness analysis with a realistic adversarial evasion attack vector. The proposed methodology was used to evaluate three supervised algorithms, Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), and one unsupervised algorithm, Isolation Forest (IFOR). Constrained adversarial examples were generated with the Adaptative Perturbation Pattern Method (A2PM), and evasion attacks were performed against models created with regular and adversarial training. Even though RF was the least affected in binary classification, XGB consistently achieved the highest accuracy in multi-class classification. The obtained results evidence the inherent susceptibility of tree-based algorithms and ensembles to adversarial evasion attacks and demonstrates the benefits of adversarial training and a security by design approach for a more robust IoT network intrusion detection and cyber-attack classification.