Objective: An intrusion detection system was constructed on the basis of the characteristics of BP neural network model. Methods: According to the capture engine of the text, all network data stream flowed through the systematic monitoring network segment will be captured, feature extraction module analyze and process the captured network data flow, you can extract complete and accurate eigenvector on behalf of this data stream, and this eigenvector will be presented to the neural network classification engine, as the input vector of a neural network. Results: The neural network classification engine analyzes and processes this eigenvector, and thus distinguishes whether it is the intrusive action.
A simple and easy hydrothermal process has been employed to synthesize flower-like ZnO products consisting of numerous orderly oriented and bundled nanorods. The structure and morphology of the novel ZnO structure are characterized in detail. The flower-like ZnO-nanorod-based gas sensors are investigated for their ethanol-sensing properties, and the results reveal that the sensors exhibit a high response of 143.6 to 1000 ppm ethanol and good selectivity at the optimal operating temperature of 250 ı C. The effect of the flower-like morphology on the response of the gas sensors to ethanol is also investigated.
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