2022 Vol.5 Dec.No4

• Title: Intrusion detection of high dimensional seismic data storage platform based on average membership
• Name: Phillips Quella
• Company: California American University,USA.
• Abstract:

  Aiming at the problems existing in the intrusion analysis and detection process of Marine Internet of things high-dimensional seismic data storage platform, a intrusion detection method based on average membership degree is proposed. Under the high dimensional seismic data storage platform, real-time operation data in the platform are collected by means of the installed collector. Based on the collected and processed data, the characteristics of normal behavior and intrusion behavior are extracted. The average membership degree function is constructed, and the average membership degree related to the platform behavior characteristics is solved, and the specific value of the behavior characteristics is adjusted, which is used as the standard of intrusion behavior detection. The real-time operation data of the platform is matched with the detection standard, so as to realize the intrusion detection of the high-dimensional seismic data storage platform. The results show that compared with the traditional intrusion detection method, the average detection error rate of the design method is reduced by 0.13%.

  
  

• Keyword: Mean membership; High dimensional seismic data; Data storage platform; Intrusion behavior detection
• DOI: 10.12250/jpciams2022090515
• Citation form: Phillips Quella.Intrusion detection of high dimensional seismic data storage platform based on average membership [J]. Computer Informatization and Mechanical System,2022,Vol.5,pp.64-67

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