# Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things

arXiv: https://arxiv.org/abs/1911.05771 · HTML: https://mlsanity.com/paper/1911.05771/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 18 of 18)

- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection](https://mlsanity.com/paper/2111.13149/) (2021)
- [Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification](https://mlsanity.com/paper/2301.13122/) (2023)
- [TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security](https://mlsanity.com/paper/2205.01232/) (2022)
- [Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2212.12121/) (2022)
- [Federated PCA on Grassmann Manifold for IoT Anomaly Detection](https://mlsanity.com/paper/2407.07421/) (2024)
- [A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks](https://mlsanity.com/paper/2505.21703/) (2025)
- [Digital Twin-based Intrusion Detection for Industrial Control Systems](https://mlsanity.com/paper/2207.09999/) (2022)
- [A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data](https://mlsanity.com/paper/2501.15365/) (2025)
- [Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks](https://mlsanity.com/paper/2607.00553/) (2026)
- [Intrusion Detection in Internet of Things using Convolutional Neural Networks](https://mlsanity.com/paper/2211.10062/) (2022)
- [IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection](https://mlsanity.com/paper/2401.01343/) (2023)
- [ADDAI: Anomaly Detection using Distributed AI](https://mlsanity.com/paper/2205.01231/) (2022)
- [A Deep Multi-Modal Cyber-Attack Detection in Industrial Control Systems](https://mlsanity.com/paper/2304.01440/) (2023)
- [Anomaly Detection Dataset for Industrial Control Systems](https://mlsanity.com/paper/2305.09678/) (2023)
- [Securing Swarms: Cross-Domain Adaptation for ROS2-based CPS Anomaly Detection](https://mlsanity.com/paper/2508.15865/) (2025)
- [Pro-ZD: A Transferable Graph Neural Network Approach for Proactive Zero-Day Threats Mitigation](https://mlsanity.com/paper/2602.07073/) (2026)
- [Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detection](https://mlsanity.com/paper/2610.02552/) (2026)

## Abstract (arXiv)

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a machine learning based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.