基于 GAF-DenseNet 的航空发动机虚假数据注入攻击检测

摘要

A machine learning detection method for aero-engine system false data injection attacks based on Gramian angular field (GAF) and densely connected convolutional networks (DenseNet) was proposed. Firstly，two attack models of continuous and interval spurious data injection were constructed based on the simulation dataset of NASA’s commercial modular aero-propulsion system simulation (C-MAPSS). Secondly，the GAF method was proposed to transform the timing signal obtained by the aeroengine sensors into the image signal，and a DenseNet-121 network was designed to detect whether the aero engine was subject to false data injection attack and the type of attack was identified. Finally，the average classification accuracy of GAF-DenseNet method on T24，T50，and P30 sensors was 98.46%，which was 1.91%，3.82%，and 0.38% better compared with long and short-term memory，gated recurrent units，and convolutional neural networks，respectively. ? 2023 BUAA Press.

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