Analyzing the Security Threat Posed by an Intelligent Internet-of-Things System with Split Analytic Functions

In the context of intelligent Internet-of-Things (IoT) systems, security can be viewed as preserving symmetry among trusted entities, accurate physical observations, and secure digital interactions despite adversarial threats. Analyzing the security of analytic functions in intelligent IoT systems is vital because these systems process sensitive data and make critical real-time decisions. Evaluating their security prevents data theft, stops malicious model manipulation, and protects physical infrastructure from dangerous cyber–physical attacks. Research on the security of layered intelligent IoT architecture focuses on identifying vulnerability points across physical, network, edge/fog, and cloud intelligence layers to design scalable, artificial intelligence-driven, and resource-aware defense mechanisms. This paper provides a security analysis of an intelligent IoT system architecture with split analytic functions based on their time criticality. By identifying the system’s main analytics components and interfaces, we distinguish attack surfaces, analyze the main threats and security controls, and propose security solutions that mitigate the risk. We formally modeled the threat of an insecure peering connection between analytics components to describe the attack flow. To model the attack scenarios, we considered three attacker profiles (low, medium, and high) and calculated the total risk for each. The results show that the log-normal firewall configuration offers high resistance to low-capability attackers but can collapse under 17.2 h against advanced threats. Security teams face a severe reduction in detection time, with windows shrinking from a median of 12.7 days to just 33 h when defending against high-capability attackers. Furthermore, we provide a brief sensitivity analysis examining the effects of key distribution parameters on the TTC results.