Abstract
In the era of big data, information-based Internet life has brought people many conveniences, but the resulting series of network security problems are also severe, which brings great inconvenience to the regular use of the network environment. The abnormal network traffic detection is currently carried out mainly through intrusion detection systems. However, the current traffic intrusion detection systems have many shortcomings, and the system resource occupancy rate is relatively high, so its actual process needs to be upgraded. This paper uses deep learning technology and clustering algorithm to upgrade the traditional traffic detection algorithm to make it more efficient.
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