Semantic-based defense mechanism for ai model networks using rich semantic identifier mapping

As AI models rapidly advance, they face increasing network threats. Distributed AI training lacks effective security mechanisms; current centralized networking schemes focus on transmission speed but fail to ensure communication security for distributed servers. When AI models provide online services, servers can suffer from DDoS and man-in-the-middle attacks, leading to service outages or privacy breaches due to reliance on the traditional TCP/IP system. These security issues can be mitigated by designing a more robust identifier network system. Mainstream solutions like RoCE and Infiniband lack identifier-related message security, and the TCP/IP protocol stack has weak identification mechanisms. Therefore, research on AI networking and traditional network identification management is crucial. This paper introduces a semantic-rich identifier mapping (SRIM) management mechanism, which uses multi-dimensional identifiers to enhance network communication security. We designed a network layer packet addressing process based on semantic-rich identifiers to improve message transmission security in AI networks. This mechanism also supports AI service providers’ access control strategies, enabling rapid identification and interception of common network attacks through identifier features. The SRIM mechanism’s communication interface with control entities allows them to block malicious users’ data flows at the network layer by issuing correlation verification policies. Finally, simulations and analyses demonstrate SRIM’s security advantages.