Hardware asset classification method, system, and device, and readable storage medium
A technology of asset classification and hardware, which is applied in the direction of computer parts, instruments, characters and pattern recognition, etc., can solve the problems of insufficient consideration, one-sided grouping results, inability to classify similar hardware assets into the same asset group, etc., and achieve accurate feature description , the effect of accurate classification results
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Embodiment 1
[0060] The following combination figure 1 , figure 1 It is a flow chart of a hardware asset classification method provided in the embodiment of this application, which specifically includes the following steps:
[0061] S101: Obtain the dynamic attributes and static attributes of each hardware asset in the network;
[0062] This step aims to obtain multiple characteristic information of each hardware asset in the network, specifically dynamic attributes (that will change with time) and static attributes (that will not change with time), wherein the dynamic attributes and static attributes are It can include a variety of specific characteristic parameters. For example, static attributes can include: IP address (static IP is usually used in the enterprise, and the situation of dynamic IP is not considered here), vulnerability information, motherboard firmware version, and at least one of the system activation time. The dynamic attributes may include: the number of alarm inform...
Embodiment 2
[0076] The following combination figure 2 , figure 2 The flow chart of another hardware asset classification method provided by the embodiment of this application is different from the first embodiment. This embodiment also adds a scheme for calculating the similarity value of each asset group, and based on the similarity of each asset group Value pairs are screened whether they meet the preset similarity requirements, in order to further improve the similarity of each hardware asset in the asset group. The specific steps are as follows:
[0077] S201: Obtain dynamic attributes and static attributes of each hardware asset in the network;
[0078] S202: Calculate the comprehensive characteristic parameters of each hardware asset according to the dynamic attributes and static attributes of each hardware asset;
[0079] S203: Classify each comprehensive feature parameter to obtain a preset number of asset groups;
[0080] S204: Calculate the similarity value of each asset gr...
Embodiment 3
[0085] The following combination image 3 , image 3 The flow chart of another hardware asset classification method provided by the embodiment of this application is different from the first embodiment. This embodiment sets corresponding weighted values for different types of feature information according to the degree of influence, in order to combine the weighted The weights are calculated according to the weighted method to obtain a comprehensive feature parameter with more accurate feature description, and the K-Means clustering algorithm is used for classification, and based on the K-Means clustering algorithm, it provides a method for adding new hardware assets on a small scale. The way to add it to the appropriate asset group includes the following steps:
[0086] S301: Obtain dynamic attributes and static attributes of each hardware asset in the network;
[0087]S302: Obtain a similarity parameter for each attribute representing the similarity degree between each h...
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