基于时序特征数据高效索引技术的物联网感知设备安全自动监测技术
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引用本文:王伟,尚东方,韩雪.基于时序特征数据高效索引技术的物联网感知设备安全自动监测技术[J].计算技术与自动化,2024,(1):61-65
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作者单位
王伟,尚东方,韩雪 (交通运输部天津水运工程科学研究所天津 300456) 
中文摘要:物联网设备已经被广泛应用于各个领域,为保证物联网的安全,排除内部隐患,基于时序特征数据高效索引技术设计物联网感知设备安全自动监测方法。结合时序特征数据高效索引技术提取物联网信息特征,在报文传输过程的基础上,区分不同流量数据之间的差异、恶意攻击软件与感知设备的系统特征,计算样本数据的表征值,得到物联网感知设备的原始信息特征。对数据特征进行分类,计算其数据内的缺失值和错误值,得到特征向量的筛选优化结果,计算训练损失函数,调整实际操作的阈值,保证数据特征分类的准确性。搭建物联网感知设备监测模型,训练判别器,进行物联网的自动监测。分别对数据包、字节以及数据流量进行识别,该监测技术可以准确地区分良性数据与攻击数据,从而保证物联网感知设备的安全。
中文关键词:时序特征数据  高效索引技术  物联网  感知设备安全  自动监测技术
 
Automatic Security Monitoring Technology of IoT Sensing Devices Based on Efficient Indexing Technology of Time Series Characteristic Data
Abstract:IoT devices have been widely used in various fields. In order to ensure the security of the Internet of Things and eliminate internal hidden dangers, an automatic security monitoring method for IoT sensing devices is designed based on efficient indexing technology of time series feature data. Combined with the efficient indexing technology of time series feature data to extract the networking information features, on the basis of the message transmission process, distinguish the differences between different traffic data, the characteristics of the system between malicious attack software and sensing devices, calculate the representative values of sample data, and obtain the original information features of the sensing devices of the Internet of Things. Classify the data features, calculate the missing and wrong values in the data, get the screening and optimization results of feature vectors, calculate the training loss function, adjust the threshold value of actual operation, and ensure the accuracy of data feature classification. Build the monitoring model of IoT sensing devices, train discriminators, and conduct automatic monitoring of the Internet of Things. Data packets, bytes and data traffic are identified respectively. This monitoring technology can accurately distinguish benign data and attack data, so as to ensure the security of IoT sensing devices.
keywords:time series characteristic data  efficient indexing technology  internet of things  perceive device security  automatic monitoring technology
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