A Multi-Layer Security Architecture for Smart-City IoT with Measured Limits: Edge Intrusion Detection, Screened Federated Learning and Ledger-Anchored Model Provenance

Abstract

Smart-city platforms connect IoT devices across transport, energy, water, healthcare and e-government, so every gateway is an entry point. This paper evaluates a four-layer architecture—edge intrusion detection trained across districts by federated learning, digests anchored on a permissioned ledger, and a cloud orchestrator that correlates and audits—whose contribution, LPRA, is a screening rule with ledger-recorded accept and quarantine decisions. On 211,043 identifier-free IoT flows, a depth-8 tree reached F1 = 0.997 at 17.7 KB and an estimated 0.19 J per million flows. Federated learning across five districts reached 0.971 ± 0.001, against 0.952 ± 0.002 alone. Under scaling, label-flip, Gaussian-noise and stealth poisoning attacks, an adaptive attacker that knows the screen, and a sign-flip attack not used in its design, LPRA matched the best robust baselines under attack, avoided their no-attack heterogeneity tax at five districts and produced a ledger-writable decision; it did not detect the stealth attack, its screen holds to a Byzantine fraction of about 0.36, degrades at 0.44–0.45 and inverts above one half, and the ledger’s guarantee ends at a validator-quorum collusion. Anchoring ran at 146 tx/s on a two-organisation Hyperledger Fabric test network. F1 falls from 0.96 within a dataset to 0.12 across, and from 0.9824 to 0.0005 on a later day; the federated model transfers worse than the edge tree. Training must stay local and continuous; federation delivers it.