Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach. (arXiv:2301.09801v1 [cs.CR])

Data scarcity hinders the usability of data-dependent algorithms when
tackling IoT intrusion detection (IID). To address this, we utilise the data
rich network intrusion detection (NID) domain to facilitate more accurate
intrusion detection for IID domains. In this paper, 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 interrelationships, respectively. The …

address algorithms alignment data detection domain domains intrusion intrusion detection iot knowledge network scarcity usability