A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks
This addresses cybersecurity risks for researchers and developers in wireless networks, but it appears incremental as it applies an existing tool to a specific domain.
The paper tackles the problem of AI model vulnerabilities like poisoning in next-generation wireless networks by proposing a Streamlit-based trust platform for evaluating and defending against adversarial threats.
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.