Virus propagation model based on log feature detection

Yungang Qian; Jianguo Ren

doi:10.1117/12.2678852

12 May 2023 Virus propagation model based on log feature detection

Yungang Qian, Jianguo Ren

Proceedings Volume 12641, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2023); 126410L (2023) https://doi.org/10.1117/12.2678852
Event: International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2023), 2023, Changsha, China

Abstract

Based on the problem that existing antivirus software cannot effectively respond to emerging viruses and lacks real-time detection and effective interception of viruses, a class of detection propagation models based on log feature detection of viruses is established based on the classical susceptible-exposed-infected-immune (SEIR) propagation model with the introduction of containment nodes. The experimental results show that the drop rate of infected nodes of this model is increased by 26.45%, 40.68%, and 54.24% when the detection rate is taken as 0.5, 0.7, and 0.9, respectively, compared with the previous SEIR model. Therefore, the detection propagation model based on the detection of log feature information of viruses can effectively contain and remove the spread of new viruses in the network, and the higher the detection rate, the better the effect.

Citation Download Citation

Yungang Qian and Jianguo Ren "Virus propagation model based on log feature detection", Proc. SPIE 12641, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2023), 126410L (12 May 2023); https://doi.org/10.1117/12.2678852

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