Offline-Capable AI–Blockchain Architecture for Biochemical Threat Detection in Mission-Critical MANET Environments

Biochemical threats remain a serious concern in mission-critical environments, particularly those characterized by intermittent connectivity and infrastructure degradation. Traditional centralized detection systems are ill-suited for such conditions, as they depend on stable communication channels and are inherently vulnerable to cyber–physical disruptions. This work introduces a decentralized solution integrating artificial intelligence (AI) and blockchain (BC) for autonomous biochemical threat detection within tactical mobile ad hoc networks (MANETs). The framework uses a random forest (RF) classifier trained on acetylcholinesterase (AChE) sensor data to identify sarin exposure with 100% accuracy and sub-25-ms inference latency. Threat verification is secured using a lightweight proof of authority and association ( PoA2 ) BC, which provides tamper-resistant logging and distributed consensus. The architecture supports offline operations and maintains functionality under conditions of 20% packet loss and node disruption. Simulations conducted in degraded network environments confirmed the system’s robustness and scalability, establishing it as a resilient and efficient platform for secure biochemical threat detection in dynamic, resource-constrained mission-critical settings.