消费行为数据采集平台的安全保障与预测模型研究

Research on Security Protection and Prediction Models for Consumer Behavior Data Collection Platforms

依据用户浏览记录等信息进行兴趣爱好的预测并进行合理推荐,已成为诸多销售平台优化用户体验的常用手段,而用户信息安全问题自然也成了各大平台面临的一大挑战.提出一种基于内生安全的消费行为数据采集与分析平台,通过采集用户数据,使用基于长短时记忆网络的预测模型,精准预测未来销售流量数据.在数据安全性方面,平台使用基于内生安全的拟态云WAF,通过动态选择算法、异构执行体和裁决算法3种核心技术为整个数据平台提供了自主可控的安全保障,并利用基于Sketch的网络测量技术对异常流量进行了检测.此外,平台融合了数据备份和恢复、加密存储、数据传输加密技术,并对重要的数据采取分类存储、访问控制等措施.多项对比实验验证表明,用于中烟销售流量的预测平台相较于目前提出的多种技术在预测准确度和数据安全方面都有显著提升,可为企业销量预测提供一种合理可行的解决方案.

Predicting interests and making reasonable recommendations based on user browsing records and other information has become a common means for many sales platforms to optimize the user experience.Thus,the issue of user information security has naturally become a major challenge for major platforms.This paper proposes an endogenous securitybased consumer behavior data collection and analysis platform,which accurately predicts future sales traffic data by collecting user data and using a prediction model based on long and shortterm memory networks.In terms of data security,the platform uses endogenous securitybased mimetic cloud WAF,providing autonomous and controllable security for the entire data platform through three core technologies:dynamic selection algorithm,heterogeneous executables,and adjudication algorithm,and detects anomalous traffic by utilizing sketchbased network measurement techniques.In addition,the platform incorporates data backup and recovery,encrypted storage,and data transmission encryption technologies,and takes measures such as categorized storage and access control for important data.Extensive experiments demonstrate that the prediction platform used for China Tobacco’s sales traffic has significant improvement in prediction accuracy and data security when compared with existing techniques,and can provide a reasonable and feasible solution for enterprise sales prediction.