Generative AI (GAI) models have been rapidly advancing, with a wide range of applications including intelligent networks and mobile AI-generated content (AIGC) services.
Despite their numerous applications and potential, such models create opportunities for novel security challenges.
In this paper, we examine the challenges and opportunities of GAI in the realm of the security of intelligent network AIGC services such as suggesting security policies, acting as both a ``spear'' for potential attacks and a ``shield'' as an integral part of various defense mechanisms.
First, we present a comprehensive overview of the GAI landscape, highlighting its applications and the techniques underpinning these advancements, especially large language and diffusion models.
Spear or Shield: Leveraging Generative AI to Tackle Security Threats of Intelligent Network Services · Around
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K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y.-J. A. Zhang, “The roadmap to 6G: AI empowered wireless networks,” IEEE Commun. Mag. , vol. 57, no. 8, pp. 84–90, Aug. 2019
2019
Earlier work this paper cites.
S. De, M. Bermudez-Edo, H. Xu, and Z. Cai, “Deep generative models in the industrial internet of things: A survey,” IEEE Trans. Industr. Inform. , vol. 18, no. 9, pp. 5728–5737, Sept. 2022
S.-Y. Chou, P.-Y. Chen, and T.-Y. Ho, “How to backdoor diffusion models?” in ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learning , 2022
H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, and D. I. Kim, “AI-generated incentive mechanism and full-duplex semantic communications for information sharing,” IEEE J. Sel. Areas Commun. , to appear, 2023
S. Ghalebikesabi, L. Berrada, S. Gowal, I. Ktena, R. Stanforth, J. Hayes, S. De, S. L. Smith, O. Wiles, and B. Balle, “Differentially private diffusion models generate useful synthetic images,” arXiv preprint arXiv:2302.13861 , 2023