计算机应用研究2016，Vol.33Issue(4)：1137-1140,4.DOI:10.3969/j.issn.1001-3695.2016.04.039
基于迁移学习和D-S理论的网络异常检测
New network anomaly detection using transfer learning and D-S theory
摘要
Abstract
The current approaches of anomaly detection cannot effectively detect unknown network attacks,which follow the same or different distribution.To solve this problem,this paper proposed a new network anomaly detection using transfer learning technique and D-S theory.At first,this paper created a model for known network attacks with transfer learning meth-od,which considered the distinctions in anomaly attacks following different distribution.Secondly,combined with D-S theory, the classifier could pinpoint unknown network attacks as outliers.The results show that the proposed detection approach has a higher detection rate for unknown network anomalies.关键词
迁移学习/D-S理论/异常行为分析/数据融合Key words
transfer learning/D-S theory/abnormal behavior analysis/data fusion分类
信息技术与安全科学引用本文复制引用
赵新杰,刘渊,孙剑..基于迁移学习和D-S理论的网络异常检测[J].计算机应用研究,2016,33(4):1137-1140,4.基金项目
国家自然科学基金资助项目（61103223）；江苏省自然科学基金重点资助项目 （）
