基于相邻请求的动态时间阈值会话识别算法

Session identification algorithm based on dynamic time threshold of adjacent requests

在大数据平台的异常检测分析中,为提高会话序列建模的效率,提出一种基于相邻请求的动态调整时间间隔阈值的会话识别算法——DAITS算法。首先同时结合站点页面因子和用户访问页面时间的平均因子;然后在两者间加入合适的权重因子对时间阈值进行动态调整;最后根据判断是否超过该时间阈值来划分会话。实验结果表明,DAITS算法比传统使用固定阈值的方法在会话识别的精确率和查全率上提高了14.8%和13.2%,比动态调整阈值的方法在精确率和查全率上提高了6.2%和3.2%。

Focusing on the issue of improving the efficiency of session sequence modeling in the anomaly detection analysis of big data platform, a session identification algorithm based on Dynamic Adjustive Interval Time threShold of adjacent requests （DAITS） was proposed. Firstly, the factor of website pages and the average factor of users access time to the page were combined. Then, the appropriate weighting factor was used to dynamically adjust the time threshold. Finally, the session was divided according to whether the time threshold was exceeded. The experimental results show that compared with the traditional methods of using fixed thresholds, the precision of session identification was increased by 14.8% and the recall was increased by 13. 2%; compared with the existing methods with dynamic adjustive thresholds, the precision of session identification was increased by 6.2% and the recall was increased bv 3.2%.