Data scarcity hinders the usability of data-dependent algorithms when tackling IoT intrusion detection (IID). To address this, we utilize the data-rich network intrusion detection (NID) domain to facilitate more accurate intrusion detection for IID domains. In this article, a geometric graph alignment (GGA) approach is leveraged to mask the geometric heterogeneities between domains for better intrusion knowledge transfer. Specifically, each intrusion domain is formulated as a graph where vertices and edges represent intrusion categories and category-wise inter-relationships, respectively. The overall shape is preserved via a confused discriminator incapable to identify adjacency matrices between different intrusion domain graphs. A rotation avoidance mechanism and a center point matching mechanism are used to avoid graph misalignment due to rotation and symmetry, respectively. Besides, category-wise semantic knowledge is transferred to act as vertex-level alignment. To exploit the target data, a pseudo-label (PL) election mechanism that jointly considers network prediction, geometric property, and neighborhood information is used to produce fine-grained PL assignment. Upon aligning the intrusion graphs geometrically from different granularities, the transferred intrusion knowledge can boost IID performance. Comprehensive experiments on several intrusion data sets demonstrate state-of-the-art performance of the GGA approach and validate the usefulness of GGA-constituting components.