摘要
针对传统的大规模船舶物联网安全状态识别方法在非法攻击情况下,网络振荡引起的识别中断问题,提出大数据背景下的大规模船舶物联网安全状态识别研究。利用数据采集卡采集物联网中多个传感器数据,通过通信网关传输至处理中心,融合多源数据,在Spark框架下实现识别任务的调度和资源分配,依据状态转移规则计算出融合数据的状态转移最大概率,识别出物联网安全状态。测试结果表明,与传统的识别方法相比,提出的大数据背景下的大规模船舶物联网安全状态识别方法识别过程并未中断,并且有效响应时间更长。
Aiming at the problem of identification interruption caused by network oscillation under the traditional largescale marine IoT security state recognition method under illegal supply,a large-scale marine IoT security state recognition study under the background of big data is proposed.Use the data acquisition card to collect multiple sensor data in the Internet of Things,transmit it to the processing center through the communication gateway,merge the multi-source data,realize the scheduling and resource allocation of the identification task under the Spark framework,and calculate the state transition of the fusion data according to the state transition rules Maximum probability to identify the safe state of the Internet of Things.The test results show that,compared with the traditional identification method,the identification process of the largescale ship IoT security state identification method under the background of big data is not interrupted,and the effective response time is longer.
作者
秦健勇
代康
QIN Jian-Yong;DAI Kang(Xinjiang Institute of Engineering,Urumqi 830011,China)
出处
《舰船科学技术》
北大核心
2020年第18期190-192,共3页
Ship Science and Technology
基金
新疆科技厅资助项目(2019D01A31)
关键词
大数据
物联网
安全状态
识别
big data
physical network
safe state
identify
