Title :
Anomaly Traffic Detection Model Based on Dynamic Aggregation
Author :
Sun, Zhixin ; Gong, Jin
Author_Institution :
Coll. of Comput., Nanjing Univ. of Posts & Telecommun., Nanjing, China
Abstract :
Integrated with the ideas of aggregation and network model, this paper presented an anomaly detection model based on DAATDM, i.e. the dynamic and aggregate anomaly detection model. Besides, it established an anomaly traffic detection system based on DAATDM. DAATDM not only analyzed the aggregation of network parameters but also built a weighted statistical model for aggregate parameters which can be set dynamically. DAATDM can adjust its dependency on network parameters so as to enhance the flexibility of anomaly detection and identify attack features.
Keywords :
object detection; parameter estimation; statistical analysis; traffic engineering computing; DAATDM; anomaly traffic detection model; dynamic aggregation; network parameters; weighted statistical model; Aggregates; Computational modeling; Computer crime; Computers; Feature extraction; Floods; IP networks; Aggregation; Anomaly traffic; Dynamic detection;
Conference_Titel :
Electronic Commerce and Security (ISECS), 2010 Third International Symposium on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4244-8231-3
Electronic_ISBN :
978-1-4244-8231-3
DOI :
10.1109/ISECS.2010.19