Closed-Set Heterogeneous Domain Adaptation for IoT Intrusion Detection: An Anchor-Based Benchmark Across Single- and Multi-Source Transfer.

Closed-set heterogeneous domain adaptation (HDA) for Internet of Things (IoT) intrusion detection aims to transfer detection capabilities across environments that differ in devices, telemetry, feature schemas, attack implementations, label taxonomies, and target supervision availability. Although recent HDA methods report strong performance, their deployment meaning is often unclear because improvements over a weak source-only baseline do not show how much target supervision headroom has been recovered or whether adaptation is preferable to direct target-side labelling under the same budget. This paper presents a controlled, anchor-based benchmark for closed-set HDA in IoT intrusion detection. Edge-IIoTset is used as the main fixed target dataset, with transfer from CICIDS2017, UNSW-NB15, CICIDS2017 + UNSW-NB15, and CICIDS2017 + NSL-KDD under single-source and multi-source settings. The benchmark defines fixed resolved contexts, Intersection and Union representation contracts, a five-class closed-set label contract, leakage-safe preprocessing, and an anchor ladder consisting of source-only, correlation alignment (CORAL), matched-budget target-only, and oracle target-only references. Geometric Graph Alignment (GGA) and the Joint Semantic Transfer Network (JSTN) are evaluated as the primary selected native single-source semi-supervised HDA (SS-HDA) and multi-source semi-supervised HDA (MS-HDA) exemplars, while the Prototype-Matching Graph Network (PMGN) and Conditional Weighting Adversarial Network (CWAN) provide 1:10 method coverage checks. Each method-context-ratio configuration is evaluated across twenty fixed seeds, and DA-versus-target-only differences are tested using paired seed-level statistical evidence. A compact second-target confirmatory experiment using ToN-IoT assesses whether the qualitative headroom recovery and same-budget deployment patterns remain visible under a different IoT/IIoT target. The results show that primary native HDA can recover substantial source-only-to-oracle headroom, but not uniformly. At the 1:10 labelled target ratio, GGA recovers 0.633-0.835 of the available headroom across C1-C4, while JSTN recovers 0.776-0.897 in the contemporary-source MS-HDA family and 0.872-0.926 in the mixed-vintage family. Same-budget comparisons show that DA is deployment-competitive only in some contexts; in others, direct target-side supervised learning is stronger. The benchmark therefore shows that closed-set HDA should be evaluated as target-conditioned, context-resolved evidence rather than as a pooled method leaderboard.