![]() This work is focusing on attack detection which is an essential task to secure the network and the data. ![]() In the aim of providing secure data processing in the WSN, many techniques are proposed to protect the data privacy while being transferred from the sensors to the base station. However, WSN faces high security threats considering most of them are deployed in unattended nature and hostile environment. Environmental monitoring, security of buildings and precision agriculture are few example among several other fields. Due to their small size and cost-effective, WSN are attracting many industries to use them in various applications. The number of Wireless sensor network (WSN) deployments have been growing so exponentially over the recent years. Finally, the experiment proves that the network traffic anomaly monitoring system based on data mining has higher monitoring accuracy and shorter monitoring time, which is practical in practical application and fully meets the research requirements.KeywordsData miningNetwork flowAbnormal monitoring On this basis, through the system software functions of acquisition module, data processing module, data analysis module, data application module and infrastructure management module, the abnormal monitoring of network traffic is realized through data mining. Through the configuration of data acquisition equipment, analysis equipment, exception handling equipment and system management equipment, the hardware structure of the system is designed. However, due to the low monitoring accuracy and long monitoring time of the traditional network traffic anomaly monitoring system, this paper designs a network traffic anomaly monitoring system based on data mining. Therefore, in order to better ensure the security of the network structure, it is necessary to monitor the abnormal network traffic. ![]() The security hidden dangers in the network will affect the normal operation of the network.
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