YAN Hui, CAO Yuan-da. Application of Bayesian Dynamic Forecast in Anomaly Detection[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2005, 14(1): 41-44.
Citation:
YAN Hui, CAO Yuan-da. Application of Bayesian Dynamic Forecast in Anomaly Detection[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2005, 14(1): 41-44.
YAN Hui, CAO Yuan-da. Application of Bayesian Dynamic Forecast in Anomaly Detection[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2005, 14(1): 41-44.
Citation:
YAN Hui, CAO Yuan-da. Application of Bayesian Dynamic Forecast in Anomaly Detection[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2005, 14(1): 41-44.
School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China; Department of Electronic Engineering, The Academy of Equipment Command and Technology, Beijing101416, China
2.
School of Computer Software, Beijing Institute of Technology, Beijing100081, China
A macroscopical anomaly detection method based on intrusion statistic and Bayesian dynamic forecast is presented. A large number of alert data that cannot be dealt with in time are always aggregated in control centers of large-scale intrusion detection systems. In order to improve the efficiency and veracity of intrusion analysis, the intrusion intensity values are picked from alert data and Bayesian dynamic forecast method is used to detect anomaly. The experiments show that the new method is effective on detecting macroscopical anomaly in large-scale intrusion detection systems.
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