Risk Measurement Method of Fitness APP Based on Riemann Manifold
With the widespread adoption of smart devices,they have become prime targets for malicious software and malicious traffic attacks,posing significant cybersecurity risks.Fitness apps,due to the privacy and sensitivity of the data they acquire,face even more serious data security issues,making their security measurement models a key hotspot for addressing this challenge.Existing security measurement models are mostly based on static featu-res and fail to fully consider the dynamic network behavior of smart devices.To address this limitation,this paper proposes a network behavior-based security measurement model for fitness apps,utilizing covariance matrices to transform the network space,thereby enhancing the accuracy of malicious attack detection.By considering the dynamic network behavior characteristics of fitness apps,it more comprehensively reveals their security status.Furthermore,by combining Riemannian metrics,it effectively describes network security risks and computes their values,thus constructing a risk measurement model based on attack recognition and Riemannian manifolds to achieve more secure data protection.
data securitynetwork behaviorRiemannian manifoldrisk measurement modelcovariance matrix
万方数据
维普
