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- W2157084783 abstract "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-propagation neural network forecasting model, which predicts unauthorized invasions of user based on previous observations and takes further action before intrusion occurs. Our back-propagation neural network 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-propagation neural network 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." @default.
- W2157084783 created "2016-06-24" @default.
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- W2157084783 date "2004-06-10" @default.
- W2157084783 modified "2023-09-22" @default.
- W2157084783 title "Intelligent multi-agent based back-propagation neural network forecasting model for statistical database anomaly prevention system" @default.
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- W2157084783 doi "https://doi.org/10.1109/icisip.2004.1287634" @default.
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