• Journal of Korean Society of Environmental Engineers
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• v.34 no.10
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• pp.656-663
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• 2012

After the construction of waterworks automatic meter reading with 15 mm diameter digital water mater with magnetoresistance sensor developed in this study at 96 households of apartment complexes located in Incheon-City B-Gu S-Dong, the feasibility of field application of waterworks automatic meter reading was evaluated. The field application of waterworks automatic meter reading was performed from July to December in 2011, and average reception rate was as low as 84.6% due to the instable wibro networks, the existence of communication blackout and temporary malfunction of router. After the extraction of 10 households with one to five residents out of 96 households by using stratified random sampling method and analysis of domestic water use, it was found that domestic water use was significant at August and showed a decreasing trend at September, followed by increase in domestic water use at November and decrease in domestic water use at December. This phenomenon should be attributed to weather factors (temperature, humidity, etc.), which significantly affected domestic water use. Similar trend in domestic water use in terms of weather factors was obtained in case of Liter per capita day of water use after the extraction of 30 households out of 96 households by using stratified random sampling method. After analysis of Liter per capita day for 96 households, single residents increases resulted in reduction of domestic water usage by about 14% of Liter per capita day. These results might be due to the fact that domestic water usage such as laundry, beverages, catering, cleaning, etc. should be required for even the household with one resident, whereas domestic water usage for those common utilization can be significantly saved for the household with more than one resident.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.