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

TL;DR

This paper introduces a Streamlit-based AI trust platform designed for NextG wireless networks, enabling researchers to evaluate and defend AI models against cybersecurity threats such as poisoning and evasion attacks.

Contribution

It presents a novel AI trust platform that facilitates evaluation, certification, and verification of AI models specifically for NextG networks, addressing security concerns.

Findings

Platform enables effective detection of adversarial attacks.

Supports certification and verification of AI models.

Enhances security and trust in NextG AI applications.

Abstract

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.