# TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security

arXiv: https://arxiv.org/abs/2205.01232 · HTML: https://mlsanity.com/paper/2205.01232/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems](https://mlsanity.com/paper/1906.10263/) (2019)
- [Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph](https://mlsanity.com/paper/1910.01837/) (2019)
- [An Adversarial Approach for Explainable AI in Intrusion Detection Systems](https://mlsanity.com/paper/1811.11705/) (2018)
- [Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things](https://mlsanity.com/paper/1911.05771/) (2019)
- [SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach](https://mlsanity.com/paper/1904.00753/) (2019)
- [Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning](https://mlsanity.com/paper/1912.02651/) (2019)

## Built on by (most cited first, 9 of 9)

- [XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions](https://mlsanity.com/paper/2206.03585/) (2022)
- [A Survey on Explainable Artificial Intelligence for Cybersecurity](https://mlsanity.com/paper/2303.12942/) (2023)
- [Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey](https://mlsanity.com/paper/2206.04800/) (2022)
- [A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability](https://mlsanity.com/paper/2310.17944/) (2023)
- [Towards Explainable AI for Channel Estimation in Wireless Communications](https://mlsanity.com/paper/2307.00952/) (2023)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
- [Explainable AI for Enhancing Efficiency of DL-based Channel Estimation](https://mlsanity.com/paper/2407.07009/) (2024)
- [Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2303.01259/) (2023)

## Abstract (arXiv)

Despite AI's significant growth, its "black box" nature creates challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in IoT high-risk applications, such as critical industrial infrastructures, medical systems, and financial applications, etc. Explainable AI (XAI) has emerged to help with this problem. However, designing appropriately fast and accurate XAI is still challenging, especially in numerical applications. Here, we propose a universal XAI model named Transparency Relying Upon Statistical Theory (TRUST), which is model-agnostic, high-performing, and suitable for numerical applications. Simply put, TRUST XAI models the statistical behavior of the AI's outputs in an AI-based system. Factor analysis is used to transform the input features into a new set of latent variables. We use mutual information to rank these variables and pick only the most influential ones on the AI's outputs and call them "representatives" of the classes. Then we use multi-modal Gaussian distributions to determine the likelihood of any new sample belonging to each class. We demonstrate the effectiveness of TRUST in a case study on cybersecurity of the industrial Internet of things (IIoT) using three different cybersecurity datasets. As IIoT is a prominent application that deals with numerical data. The results show that TRUST XAI provides explanations for new random samples with an average success rate of 98%. Compared with LIME, a popular XAI model, TRUST is shown to be superior in the context of performance, speed, and the method of explainability. In the end, we also show how TRUST is explained to the user.