A flow monitoring method, 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 ensuring validity, increasing detection accuracy, and reducing feature dimensions.
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Embodiment 1
[0045] Such as figure 1 Shown:
[0046] The present disclosure provides a flow monitoring method, the method comprising:
[0047] S101: Capture network traffic of a preset duration in the network and store it in a database;
[0048] S102: Perform feature extraction on the traffic data in the database to obtain feature data;
[0049] S103: Perform feature screening on the feature data to obtain filtered data;
[0050] S104: Using a preset model to classify the filtered data.
[0051] Further, the classification results include:
[0052] The captured network traffic belongs to non-encrypted normal network traffic or VPN encrypted network traffic.
[0053] Further, the process of performing feature extraction on the traffic data in the database to obtain feature data is specifically:
[0054] Extracting a series of data with the same quintuple information in the traffic data to obtain network flow data;
[0055] Feature extraction is performed on statistical features of th...
Embodiment 2
[0091] Such as Figure 5 as shown,
[0092] The present disclosure can also provide a flow monitoring device, including:
[0093] The data capture module 201 is used to capture the network traffic of preset duration in the network and store it in the database;
[0094] A feature extraction module 202, configured to perform feature extraction on the traffic data in the database to obtain feature data;
[0095] A data screening module 203, configured to perform feature screening on the feature data 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.
Embodiment 3
[0099] 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.
[0100] The computer storage medium of the present disclosure may be implemented using semiconductor memory, magnetic core memory, magnetic drum memory, or magnetic disk memory.
[0101] 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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