A NOVEL ANOMALY DETECTION APPROACH BASED ON DATA FIELD
This paper presents a new approach to detecting attack activities. In this method, network connections were transformed into data points in the predefined feature space. The influence function was designed to quantify the influence of an object and, further, the data field was divided into positive field and negative field according to the source points category. To perform classification, all the labeled training samples were regarded as source points and a data field was built in the feature space. The influence felt by given testing point in the data field was calculated and its class was judged according to the sign and magnitude of the influence in detecting process. Experimental results demonstrate that our approach has good detection performance.
Anomaly detection Data field Influence Classification Data set
HONG-YU YANG LI-XIA XIE FENG XIE
School of Computer Science, Civil Aviation University of China, Tianjin 300300, China China Information Technology Security Certification Center, Beijing 100083, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
1105-1110
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）