An intelligent assessment method of power information intrusion tolerance based on machine learning

M. L. Huang; S. X. Guo; W. J. Song; Z. G. Liao

doi:10.1117/12.2659586

28 October 2022 An intelligent assessment method of power information intrusion tolerance based on machine learning

M. L. Huang, S. X. Guo, W. J. Song, Z. G. Liao

Proceedings Volume 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022); 1245305 (2022) https://doi.org/10.1117/12.2659586
Event: Third International Conference on Computer Communication and Network Security (CCNS 2022), 2022, Hohhot, China

Abstract

An intelligent evaluation method of power information intrusion tolerance based on machine learning is proposed to solve the problems of selection of evaluation indexes, poor evaluation accuracy and efficiency in the evaluation method of power information intrusion tolerance. An intelligent evaluation system of power information intrusion tolerance is established, and an intelligent evaluation model is established by using random forest algorithm. The random forest algorithm determines the evaluation weight according to the quantitative value of the index, sets the value of the intrusion risk function, and obtains the intelligent evaluation results of intrusion tolerance. The method studied is applied to the comparative evaluation experiment of the survivability situation of the power information physical system. The evaluation mean square deviation of the intelligent evaluation method based on machine learning is less than 0.1, the average time is 140.72ms, and the evaluation efficiency is increased by 42.51%. At 1000min, the reliability value is still 0.46, which has practical significance.

Citation Download Citation

M. L. Huang, S. X. Guo, W. J. Song, and Z. G. Liao "An intelligent assessment method of power information intrusion tolerance based on machine learning", Proc. SPIE 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022), 1245305 (28 October 2022); https://doi.org/10.1117/12.2659586

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available