摘要
随着电力企业网络技术的发展,传统和新生的日志处理系统已不能满足大数据状态下的日志分析要求,为了实现系统日志异常分析的目标,该文提出一种基于时间序列的系统异常数量集成预测算法和面向该算法的评价体系。该算法对多种分类预测算法进行集成,对收集到的日志数据进行分类预测,进而实现了以综合最优的准确度预测系统的异常数量,评价体系很好地支持了该算法的工作,算法增强了日志分析平台的安全性。
In view of that the traditional or the new log processing system can not meet therequirements of the log analysis in the current situation of big data entirely with the development of power enterprise network technology, an algorithm for estimating the number of systems based on time series and the evaluation system are presented to realize the system for the algorithm. The algorithm integrates multiple classification prediction algorithms to classify the collected log data, and then realize the purpose of forecasting the number of anomaly systems with the best accuracy. The evaluation system also supports that the algorithm can increase the security of the log analysis platform.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2017年第5期634-645,共12页
Journal of Nanjing University of Science and Technology
基金
国网公司科技项目
江苏省重大研发计划产业前瞻项目(BE2017100)
赛尔下一代互联网创新项目(NGII20160122)
关键词
日志分析
异常监测
大数据平台
集成预测算法
log analysis
anomaly detection
big data platform
ensemble forecasting algorithm
