This paper describes a framework for highly distributed real-time monitoring approach to, Database Security using Intelligent Multi-Agents. The Statistical Anomaly Prevention system employs back-propagationneural Net...
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ISBN:
(纸本)0780382439
This paper describes a framework for highly distributed real-time monitoring approach to, Database Security using Intelligent Multi-Agents. The Statistical Anomaly Prevention system employs back-propagationneuralnetworkforecasting model, which predicts unauthorized invasions of user based on previous observations and takes further action before intrusion occurs. Our back-propagationneuralnetwork model makes periodic short-term forecasts, since long-term forecasts cannot accurately predict an intrusion. We use a multivariate time series technique to forecast the hacker's behavior effectively. Our back-propagationneuralnetwork model results have been compared with traditional statistical forecasting models, and a better prediction accuracy has been observed. In order to reduce single point of failures in centralized security system, a dynamic distributed system has been designed in which the security management task is distributed across the network using Intelligent Multi-Agents.
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