Current network defense tools operate predominantly at single layers: network intrusion detection systems analyze packets and protocols; endpoint detection platforms observe process and memory activity; cryptographic identity is enforced at a certificate-authority layer separate from both. Sophisticated adversaries route their operations through the seams between these layers, and recent supply-chain and lateral-movement campaigns (SUNBURST, Volt Typhoon, and others) illustrate how single-layer defenses can be individually sound yet collectively insufficient. We present TACET, a network defense platform that integrates seven detectors and thirteen modules under a unified event bus, device registry, and cross-layer correlation engine. TACET's contributions are six novel capabilities deployed as coordinated subsystems: physical-layer device fingerprinting via software-defined radio, continuous micro-timing behavioral biometrics, weighted cross-layer identity fusion, a dynamic adaptive-deception engine, a hybrid post-quantum cryptographic attestation protocol for management channels, and real-time causal attack-graph reconstruction. The platform is implemented in approximately 38,724 lines of Python across three coordinated packages, with a continuous-learning pipeline, a four-tier graceful-degradation controller, a five-level adaptive escalation engine, and integrations with syslog/CEF, STIX/TAXII, SOAR, cloud, identity, and MDM. A retrospective analytical case study against the SUNBURST attack, grounded in the public indicators, traces the detection signals the platform would have produced at each stage of the kill chain. This paper describes the platform's architecture, subsystems, and implementation; empirical validation through deployment is identified as the subject of a companion evaluation paper.
Alexander W. Smith (Wed,) studied this question.