基于GAF-DenseNet的航空发动机虚假数据注入攻击检测  

False data injection attacks detection for aero engine system based on GAF-DenseNet

作　　者：黄鹏程 陈丽丹 祁恬[3] 张哲 马永良 高明 HUANG Pengcheng;CHEN Lidan;QI Tian;ZHANG Zhe;MA Yongliang;GAO Ming(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650504,China;Faculty of Marine Engineering,Guangzhou Maritime University,Guangzhou 510725,China;School of Automation,South China University of Technology,Guangzhou 510640,China;Guangdong Artificial Intelligence and Digital Economy Laboratory,Guangzhou 510335,China;Department of Electrical Engineering,Guangzhou City University of Technology,Guangzhou 510800,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650504 [2]广州航海学院轮机工程学院,广州510725 [3]华南理工大学自动化学院,广州510640 [4]人工智能与数字经济广东省实验室,广州510335 [5]广州城市理工学院电气工程学院,广州510800

出　　处：《航空动力学报》2023年第7期1691-1702,共12页Journal of Aerospace Power

基　　金：智能电网四川省重点实验室开放基金(2021-IEPGKLSP-KFZA01)。

摘　　要：提出一种基于格拉姆角场(Gramian angular field, GAF)和密集连接卷积网络(densely connected convolutional networks,DenseNet)的航空发动机系统遭受虚假数据注入攻击的机器学习检测方法。首先,基于美国国家航空和宇宙航行局的商用模块化航空推进系统仿真数据集(commercial modular aero-propulsion system simulation,C-MAPSS),构建了连续和间隔虚假数据注入两种攻击模型;其次,通过GAF算法,在保留原始航空发动机传感器获得的时序信号的时间依赖性的前提下,对时间序列数据进行唯一编码,并设计了DenseNet-121网络对图像阵列中内含的传感器信息进行深层挖掘,进而检测航空发动机是否遭受虚假数据注入攻击及攻击类型识别;最后,融合GAF-DenseNet方法在T24、T50和P30传感器上的平均分类准确率为98.46%,与长短期记忆、门控循环单元和卷积神经网络对比分别提高了1.91%、3.82%和0.38%。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 aero-engine 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.

关 键 词：航空发动机 商用模块化航空推进系统仿真数据(C-MAPSS) 虚假数据注入攻击 格拉姆角场(GAF) 密集连接卷积网络(DenseNet) 

分 类 号：V263.6[航空宇航科学与技术—航空宇航制造工程] TP18[自动化与计算机技术—控制理论与控制工程]