基于黎曼流形的健身APP风险度量方法

Risk Measurement Method of Fitness APP Based on Riemann Manifold

随着智能设备的普及,其应用系统已成为恶意软件攻击的主要目标,存在巨大的网络安全隐患。健身App因其获取数据的隐私性和敏感性,面临的数据安全问题更加严峻,其安全度量模型成为解决这一挑战的关键点。目前的安全度量模型多数基于静态特征构建,未能全面考虑智能设备的动态网络行为。为了弥补这一不足,提出一种基于网络行为的健身App安全度量模型,运用协方差矩阵对网络空间进行转换,提高了对恶意软件攻击识别的准确率,根据健身App的动态网络行为特征,更全面地揭示了其安全状态,同时结合黎曼度量,有效描述了网络安全风险,并计算其值,从而构建出一个基于恶意软件攻击识别与黎曼流形的风险度量模型,以实现更安全的数据保护。

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.