Traffic monitoring method and device, equipment and medium
A traffic monitoring and network traffic technology, applied in the Internet field, can solve problems such as password leakage, and achieve the effect of improving the accuracy of prediction
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Embodiment 1
[0042] Such as figure 1 Shown:
[0043] The present disclosure provides a flow monitoring method, the method comprising:
[0044] S101: capturing network traffic of a preset duration in the network to form a pcap file;
[0045] S102: Perform feature construction on the captured pcap file to form a data set in the form of a two-dimensional matrix;
[0046] S103: Perform feature screening on the data set to obtain filtered data;
[0047] S104: Using a preset model to classify the filtered data.
[0048] Further, the feature construction of the captured pcap file described in S102 is specifically:
[0049] Feature construction is performed on the captured pcap file by means of flow construction and / or subdivision construction.
[0050] Further, the process of flow construction specifically includes:
[0051] The captured network traffic is divided into different flows through the five-tuple information, and the time-related characteristics in the flow are counted as the cha...
Embodiment 2
[0091] Such as Figure 5 as shown,
[0092] The present disclosure can also provide a flow monitoring device, including:
[0093] A data capture module 201, configured to capture network traffic of a preset duration in the network to form a data file;
[0094] The feature extraction module 202 is configured to perform feature construction on the captured data files to form a data set in the form of a two-dimensional matrix;
[0095] A data screening module 203, configured to perform feature screening on the data set to obtain filtered data;
[0096] The data classification module 204 is configured to use a preset model to classify the filtered data.
[0097] Wherein, the data capture module 201 in this disclosure is connected with the feature extraction module 202 , the data screening module 203 and the data classification module 204 in sequence.
[0098] Wherein, in the feature extraction module 202, the captured data files are specifically constructed by means of stream ...
Embodiment 3
[0112] The present disclosure can also provide a computer storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it is used to realize the steps of the above flow monitoring method.
[0113] The computer storage medium of the present disclosure may be implemented using semiconductor memory, magnetic core memory, magnetic drum memory, or magnetic disk memory.
[0114] Semiconductor memory, mainly used in computers, mainly has two types of semiconductor memory elements: Mos and bipolar. Mos components are highly integrated, the process is simple but the speed is slow. Bipolar components are complex in process, high in power consumption, low in integration but fast in speed. After the advent of NMos and CMos, Mos memory began to play a major role in semiconductor memory. NMos is fast, for example, the access time of Intel's 1K-bit SRAM is 45ns. CMos consumes less power, and the 4K-bit CMos static memory access time is 3...
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