A Diversity Metric Based Study on the Correlation between Diversity and Security

Abstract

Software diversity can be utilized in cyberspace security to defend against the zero-day attacks. Existing researches have proved the effectiveness of diversity in bringing security benefits, but few of them touch the problem that whether there is a positive correlation between the security and the diversity. In addition, there is little guidance on how to construct an effective diversified system. For that, this paper develops two diversity metrics based on system attribute matrix, proposes a diversity measurement and verifies the effectiveness of the measurement. Through several simulations on the diversified systems which use voting strategy, the relationship between diversity and security is analyzed. The results show that there is an overall positive correlation between security and diversity. Though some cases are against the correlation, further analysis is made to explain the phenomenon. In addition, the effect of voting strategy is also discussed through simulations. The results show that the voting strategy have a dominant impact on the security, which implies that security benefits can be obtained only with proper strategies. According to the conclusions, some guidance is provided in constructing a more diversified as well as securer system.

A Microservice Resilience Deployment Mechanism Based on Diversity

“…To mitigate shared vulnerabilities under the microservice architecture, we have established a diversified microservices deployment model to maximize the resilience of the system. Combined with the problem of load balancing, a microservice deployment algorithm based on load balancing and diversity is proposed, which reduces the service's dependence on the underlying mirror by abundant diversity and avoids the overreliance of microservices on a single node through decentralized deployment, while taking into account load balancing [31].…”

Security Enhancement Through Compiler-Assisted Software Diversity With Deep Reinforcement Learning

“…Measuring the variants' diversity. The authors ofLiu et al (2018),Gu et al (2017), andTong et al (2019) divided variants into components such as processor, operating system, application software, protocol stack, etc., and measured the differences of components separately. From a security perspective, the works ofZhang et al (2020), andLi et al (2018) used symbiosis vulnerabilities to compare the similarity between the two variants.…”