# An Approach To Enhance IoT Security In 6G Networks Through Explainable AI

arXiv: https://arxiv.org/abs/2410.05310 · HTML: https://mlsanity.com/paper/2410.05310/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [An Adversarial Approach for Explainable AI in Intrusion Detection Systems](https://mlsanity.com/paper/1811.11705/) (2018)
- [Explaining Network Intrusion Detection System Using Explainable AI Framework](https://mlsanity.com/paper/2103.07110/) (2021)
- [Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model](https://mlsanity.com/paper/2308.00074/) (2023)
- [Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection](https://mlsanity.com/paper/2104.07183/) (2021)
- [Open RAN Security: Challenges and Opportunities](https://mlsanity.com/paper/2212.01510/) (2022)

## Built on by (most cited first, 1 of 1)

- [Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks](https://mlsanity.com/paper/2505.14659/) (2025)

## Abstract (arXiv)

Wireless communication has evolved significantly, with 6G offering groundbreaking capabilities, particularly for IoT. However, the integration of IoT into 6G presents new security challenges, expanding the attack surface due to vulnerabilities introduced by advanced technologies such as open RAN, terahertz (THz) communication, IRS, massive MIMO, and AI. Emerging threats like AI exploitation, virtualization risks, and evolving attacks, including data manipulation and signal interference, further complicate security efforts. As 6G standards are set to be finalized by 2030, work continues to align security measures with technological advances. However, substantial gaps remain in frameworks designed to secure integrated IoT and 6G systems. Our research addresses these challenges by utilizing tree-based machine learning algorithms to manage complex datasets and evaluate feature importance. We apply data balancing techniques to ensure fair attack representation and use SHAP and LIME to improve model transparency. By aligning feature importance with XAI methods and cross-validating for consistency, we boost model accuracy and enhance IoT security within the 6G ecosystem.