Deterministic Sub-Microsecond Cyber-Physical Threat Mitigation: A Heterogeneous Edge AI and eBPF/XDP Architecture

Contemporary Network Intrusion Detection Systems (NIDS) and Security Informationand Event Management (SIEM) platforms rely predominantly on userspace packet buffers(AF PACKET, NFQUEUE) or retrospective cloud log ingestion pipelines. These paradigms intro-duce non-deterministic decision latencies ranging from 5.0 milliseconds to 60 seconds. Incritical Cyber-Physical Systems (CPS)—including electrical distribution substations, medicaldiagnostic networks, and maritime avionics—this temporal window is sufficient to permitunrecoverable physical actuator manipulation and mechanical damage.This paper introduces the architecture, mathematical formalization, and empirical eval-uation of Sentinel, a sovereign active defense appliance engineered in native ISO C++20and Linux kernel eBPF/XDP (Extended Berkeley Packet Filter / eXpress Data Path). Byexecuting quantized neural threat representations across heterogeneous edge silicon (IntelOpenVINO CPU/NPU and NVIDIA TensorRT GPU) and coupling classification outputsdirectly to driver-level XDP filter hash maps, the system achieves inline packet mitigation in0.84 μs at sustained throughputs exceeding 1,250,000 events per second (EPS).We present the SLAB dynamic self-describing binary wire protocol, permitting zero-copycross-evaluation of heterogeneous feature spaces without memory reallocation. Furthermore,we formalize an on-premise continual learning feedback loop leveraging Masked Autoencoding(MAE) bounded by an immutable regression safety gate that eliminates model poisoning inair-gapped networks. Evaluated against the standard Canadian Institute for Cybersecurity(CIC-IDS-2017) benchmark dataset on industrial bare-metal hardware, Sentinel demonstratesa 5,000× reduction in mitigation latency, a 90% reduction in memory utilization, and zerocloud data egress compared to current enterprise platforms.