Smart robots’ virus defense using data mining technology-Reference-Cited by-同舟云学术

Smart robots’ virus defense using data mining technology

Published:2023-01-01 Issue:1 Volume:32 Page:
ISSN:2191-026X
Container-title:Journal of Intelligent Systems
language:en
Short-container-title:

Author:

Ye Jiao¹,Patel Hemant N.²,Meena Sankaranamasivayam³,Maaliw Renato R.⁴,Ajibade Samuel-Soma M.⁵,Keshta Ismail⁶

Affiliation:

1. College of General Education, Jilin Animation Institute , Changchun , Jilin, 130013 , China

2. Department of Computer Engineering, Sankalchand Patel College of Engineering , Visnagar , Gujarat 384315 , India

3. Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering , OMR, Chennai 600119 , India

4. College of Engineering, Southern Luzon State University , Lucban , Quezon, 4328 , Philippines

5. Department of Computer Engineering, Istanbul Ticaret University , Istanbul 34445 , Turkey

6. Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University , Riyadh, 71666 , Saudi Arabia

Abstract

Abstract In order to realize online detection and control of network viruses in robots, the authors propose a data mining-based anti-virus solution for smart robots. First, using internet of things (IoT) intrusion prevention system design method based on network intrusion signal detection and feedforward modulation filtering design, the overall design description and function analysis are carried out, and then the intrusion signal detection algorithm is designed, and finally, the hardware design and software development for a breach protection solution for the IoT are completed, and the integrated design of the system is realized. The findings demonstrated that based on the mean value of 10,000 tests, the IoT’s average packet loss rate is 0. Conclusion: This system has high accuracy, good performance, and strong compatibility and friendliness.

Publisher

Walter de Gruyter GmbH

Subject

Artificial Intelligence,Information Systems,Software

Link

https://www.degruyter.com/document/doi/10.1515/jisys-2023-0065/pdf

Reference19 articles.

1. Wang CL, Wang Y, Zeng ZY, Lin CY, Yu QL. Research on logistics distribution vehicle scheduling based on heuristic genetic algorithm. Complexity. 2021;2021(11):1–8.

2. Mallik R, Sing D, Bandyopadhyay R. GPS tracking app for police to track ambulances carrying covid-19 patients for ensuring safe distancing. Trans Indian Natl Acad Eng. 2020;5(2):181–5.

3. Ma J, Luo J. MDS symbol-pair codes from repeated-root cyclic codes. Designs Codes Cryptogr. 2022;90(1):121–37.

4. Farooq Q, Shaukat Z, Zhou T, Aiman S, Li C. Inferring Virus-Host relationship between HPV and its host homo sapiens using protein interaction network. Sci Rep. 2020;10(1):8719.

5. Wang L, Wu P. Threshold dynamics of a zika model with environmental and sexual transmissions and spatial heterogeneity. Z Für Angew Mathematik Phys. 2022;73(4):1–22.