# A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks

arXiv: https://arxiv.org/abs/2211.12851 · HTML: https://mlsanity.com/paper/2211.12851/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Towards Self-learning Edge Intelligence in 6G](https://mlsanity.com/paper/2010.00176/) (2020)
- [Adversarial Attack on DL-based Massive MIMO CSI Feedback](https://mlsanity.com/paper/2002.09896/) (2020)
- [Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction](https://mlsanity.com/paper/2105.03905/) (2021)
- [The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining](https://mlsanity.com/paper/2202.08185/) (2022)

## Abstract (arXiv)

With the rapid development and integration of artificial intelligence (AI) methods in next-generation networks (NextG), AI algorithms have provided significant advantages for NextG in terms of frequency spectrum usage, bandwidth, latency, and security. A key feature of NextG is the integration of AI, i.e., self-learning architecture based on self-supervised algorithms, to improve the performance of the network. A secure AI-powered structure is also expected to protect NextG networks against cyber-attacks. However, AI itself may be attacked, i.e., model poisoning targeted by attackers, and it results in cybersecurity violations. This paper proposes an AI trust platform using Streamlit for NextG networks that allows researchers to evaluate, defend, certify, and verify their AI models and applications against adversarial threats of evasion, poisoning, extraction, and interference.