Self-Aware Services: Using Bayesian Networks for Detecting Anomalies in Internet-based

Bronstein, Alex ; Cohen, Ira ; Das, Joydip ; Duro, Marsha ; Friedrich, Richard ; Kleyner, Gary ; Mueller, Martin ; Singhal, Sharad

HP Development Company

关键词: service management;    anomaly detection;    Bayesian networks;    on-line learning;    fault and performance management;   

RP-ID  :  HPL-2001-23R1

学科分类：计算机科学（综合）

美国|英语

来源: HP Labs

【 摘 要 】

We propose a general architecture and implementation for the autonomous assessment of health of arbitrary service elements, as a necessary prerequisite to self- control. We describe a health engine, the central component of our proposed 'Self-Awareness and Control' architecture. The health engine combines domain independent statistical analysis and probabilistic reasoning technology (Bayesian networks) with domain dependent measurement collection and evaluation methods. The resultant probabilistic assessment enables open, non-hierarchical communications about service element health. We demonstrate the validity of our approach using HP's corporate email service and detecting email anomalies: mail loops and a virus attack. We also present the results of applying on- line machine learning to this architecture, and quantify the benefits of the Bayesian network layer. Notes: 23 Pages