Towards an artificial intelligence-augmented, ECG-enabled physical exam

Shen, Charles; Muse, Evan D.

doi:10.1016/s2589-7500(21)00281-8

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…In order to ensure normal operation, related operations need to be carried out under the action of virtual machine. The server network IP is studied and planned, which is connected with SSH without password, and all components are connected under the action of switches [8].…”

Section: Build An Experimental Environmentmentioning

confidence: 99%

Design and Application of Network Security Vulnerability Detection System Based on Artificial Intelligence

Qian,

He,

Zeng

2023

Atlantis Highlights in Engineering

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In order to understand the design and application of network security vulnerability detection system, a design and application research of network security vulnerability detection system based on artificial intelligence is proposed. In this paper, firstly, aiming at the high false alarm rate of network security vulnerability detection methods, an automatic network security vulnerability detection method based on artificial intelligence is designed to improve network security. The network flow table items are obtained, and the discrete function of the feature sample classification subset is established. The obtained discrete degree value is taken as the basis of the normal behavior set of network information. On this basis, the network information features are extracted, the network security situation is described, and the network information is evaluated. From the perspective of artificial intelligence, this paper analyzes the law of network vulnerabilities and realizes automatic detection of vulnerabilities. Practice has proved that this detection method can reduce the false alarm rate of network security vulnerabilities and has high feasibility.

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Section: Build An Experimental Environmentmentioning

confidence: 99%