论文标题
基于简化的下一代无线网络的人工智能信任平台
A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks
论文作者
论文摘要
随着下一代网络中人工智能（AI）方法的快速发展和集成，AI算法在频率频谱使用，带宽，延迟和安全性方面为NextG提供了很大的优势。 NextG的一个关键特征是AI的集成，即基于自我监督算法的自学习体系结构，以提高网络的性能。预计安全的AI驱动结构还可以保护Nextg网络免受网络攻击。但是，AI本身可能会受到攻击，即攻击者针对的模型中毒，并导致违反网络安全的行为。本文提出了一个使用简化的AI信任平台，用于NextG网络，使研究人员能够评估，捍卫，认证和验证其AI模型和应用，以应对逃避，中毒，提取，提取和干扰的对抗性威胁。
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.
