A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks
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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.
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References · 7
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
- 2016Adversarial examples in the physical worldcited by 1,769
- 2020Towards Self-learning Edge Intelligence in 6Gcited by 14
- 2020Adversarial Attack on DL-based Massive MIMO CSI Feedbackcited by 9
- 2021Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Predictioncited by 5
- 2022The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retrainingcited by 3
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