摘要
在船舶安全航行中物联网通信系统起着重要作用,但是通信系统一直受到网络病毒的威胁,易出现通信内容非法窃取等问题,造成船舶操作控制失误,所以必须引入跟踪检测技术对病毒入侵行为进行检测和处置。本文分析了船用物联网中通信系统病毒跟踪检测流程,提出基于BP神经网络算法的病毒跟踪检测模型,以及通信系统病毒检测报警系统的构建方案,通过仿真实验证明病毒跟踪检测模型能够有效检测出病毒路径,并经过报警系统快速对病毒做出防御应对措施。
The internet of things communication system plays an important role in the safe navigation of ships, but the communication system has always been threatened by network viruses, and it is prone to problems such as illegal theft of communication content, causing errors in ship operation and control. Therefore, tracking and detection technology must be introduced to prevent virus intrusion. Behaviors are detected and handled. This paper analyzes the virus tracking and detection process of the communication system in the marine internet of things, and proposes a virus tracking and detection model based on the BP neural network algorithm and a construction plan for the communication system virus detection and alarm system. Simulation experiments prove that the virus tracking and detection model can effectively detect Virus path, and quickly make defensive measures against the virus through the alarm system.
作者
贾春霞
JIA Chun-xia(Beijing Information Technology College,Beijing 100101,China)
出处
《舰船科学技术》
北大核心
2021年第4期127-129,共3页
Ship Science and Technology
基金
北京信息职业技术学院校级课题(XY-XN-02-201807)
关键词
物联网
船舶
通信系统
病毒跟踪检测
internet of things
ships
communication systems
virus tracking and detection
