SaaS private data transmission method of end-to-end operation and maintenance software and hardware all-in-one machine, storage medium, equipment and computer program product

Abstract

The invention discloses an SaaS private data transmission method of an end-to-end operation and maintenance software and hardware all-in-one machine, a storage medium, equipment and a computer program product, and the method comprises the steps: collecting log information of SaaS private data transmitted in the end-to-end operation and maintenance software and hardware all-in-one machine, finding out a transmission link of each piece of SaaS private data from the log information, and transmitting the transmission link to the end-to-end operation and maintenance software and hardware all-in-one machine; the transmission links of the SaaS private data are classified according to the service types; aiming at the transmission link of the SaaS private data of each service type and combining with different types of attack strategies, searching a transmission link with the highest survivability; and when the SaaS private data is transmitted in the end-to-end operation and maintenance software and hardware all-in-one machine, encrypting the SaaS private data, and carrying out SaaS private data transmission by adopting a transmission link with the highest survivability corresponding to the same service type. According to the method, the transmission link with the highest survivability score is searched for encryption transmission of the SaaS private data, and the security of SaaS private data transmission is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of privacy data transmission, and specifically, relates to a SaaS privacy data transmission method, storage medium, device and computer program product of an end-to-end operation and maintenance software and hardware integrated machine. Background Art

[0002] With the rapid development of intelligent operation and maintenance, cloud computing technology, and big data computing, the data demand of various industries has increased dramatically, and the accumulation of central data has continuously increased the requirements for the deployment and delivery of end-to-end operation and maintenance hardware and software integrated machines. The end-to-end operation and maintenance hardware and software integrated machine simplifies the operation and maintenance process and greatly improves the operation and maintenance efficiency by integrating hardware and software resources.

[0003] The SaaS privacy data in the end-to-end operation and maintenance hardware and software integrated machine is at risk of being intercepted, tampered with or leaked when transmitted among network nodes. Attackers can interfere with the normal communication between the end-to-end operation and maintenance hardware and software integrated machine and users by intercepting, tampering with or forging data packets, thereby stealing or destroying SaaS privacy data. Summary of the invention

[0004] In response to the problems existing in the prior art, the present invention provides a SaaS privacy data transmission method, storage medium, device and computer program product of an end-to-end operation and maintenance software and hardware integrated machine. The method encrypts and transmits SaaS privacy data by searching the transmission link with the highest anti-destruction score on the end-to-end operation and maintenance software and hardware integrated machine, and performs SaaS privacy data tampering judgment on each network node in the anti-destruction link to prevent SaaS privacy data from being tampered with, thereby improving the security of SaaS privacy data transmission in the end-to-end operation and maintenance software and hardware integrated machine.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution: a SaaS privacy data transmission method for an end-to-end operation and maintenance software and hardware integrated machine, specifically comprising the following steps:

[0006] Step S1: collecting log information of SaaS privacy data transmitted in the end-to-end operation and maintenance hardware and software integrated machine, finding each transmission link of SaaS privacy data from the log information, and classifying the transmission links of SaaS privacy data according to business types;

[0007] Step S2: for each type of SaaS privacy data transmission link, combine different types of attack strategies to find a transmission link with the highest anti-destruction capability;

[0008] Step S3: When transmitting SaaS privacy data in the end-to-end operation and maintenance hardware and software integrated machine, the SaaS privacy data is encrypted, and the SaaS privacy data is transmitted using the transmission link with the highest invulnerability corresponding to the same business type.

[0009] Furthermore, step S2 includes the following sub-steps:

[0010] Step S2.1, calculate the degree, betweenness and clustering coefficient of each node in the transmission link of SaaS privacy data of each business type under each attack strategy;

[0011] Step S2.2, perform Min-Max normalization processing on each node degree, node betweenness and node clustering coefficient respectively;

[0012] Step S2.3: weight all normalized node degrees, node betweenness, and node clustering coefficients in the transmission link of each piece of SaaS privacy data to obtain the invulnerability score of the transmission link of each piece of SaaS privacy data;

[0013] Step S2.4: The transmission link with the highest invulnerability score in each service type is used as the transmission link of the service type.

[0014] Furthermore, the attack strategies in step S2 include: flooding attack strategy, confusion attack strategy, tampering attack strategy and large model stealing attack strategy.

[0015] Furthermore, the calculation process of the invulnerability score of each SaaS privacy data transmission link in step S2.3 is as follows:

[0016]

[0017] Among them, S i represents the invulnerability score of the transmission link of the i-th SaaS private data, n represents the number of network nodes in the transmission link of the i-th SaaS private data, O j represents the normalized result of the node degree of the jth network node, α represents O j The weight coefficient, K j represents the normalized result of the node betweenness of the jth network node, and β represents K j The weight coefficient, F j represents the normalized result of the clustering coefficient of the jth network node, and γ represents F j The weight coefficient of .

[0018] Furthermore, step S3 includes the following sub-steps:

[0019] Step S3.1, when transmitting SaaS privacy data in the end-to-end operation and maintenance hardware and software integrated machine, encrypt the SaaS privacy data with Base64;

[0020] Step S3.2, assigning the encrypted SaaS privacy data a socket identifier that records the transmission link with the highest invulnerability corresponding to the same service type;

[0021] Step S3.3: The encrypted SaaS privacy data is transmitted on the transmission link according to the Socket identifier, and an anti-tampering check is performed on the encrypted SaaS privacy data at each network node of the transmission link. If the check passes, the encrypted SaaS privacy data is transmitted, otherwise, the transmission is terminated.

[0022] Furthermore, the specific process of performing anti-tampering check on the encrypted SaaS privacy data in step S3.3 is: deploying fanotify components on all network nodes of the end-to-end operation and maintenance software integrated machine. When the encrypted SaaS privacy data is transmitted to a certain network node in the transmission link, if the configuration file of the fanotify component changes, the encrypted SaaS privacy data will be forwarded to the isolation area for virus feature matching, and the configuration file of the changed fanotify component will be matched for virus features. If neither of them matches the virus feature, the transmission of the encrypted SaaS privacy data will continue; otherwise, the transmission of the encrypted SaaS privacy data will be terminated.

[0023] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, including: the computer program enables the computer to execute the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine when the computer program is executed by the processor.

[0024] Furthermore, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine is implemented.

[0025] Furthermore, the present invention also provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, it implements the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] (1) The SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine of the present invention obtains the degree, node betweenness and node clustering coefficient of each node under each attack type, calculates the anti-destruction score, and obtains the transmission link with the highest anti-destruction score as the transmission link of the business type. The present invention can comprehensively evaluate the overall performance of the transmission link in the end-to-end operation and maintenance hardware integrated machine from different levels through the three indicators of node degree, node betweenness and node clustering coefficient, not only focusing on the importance of a single node, but also considering its position in the network and the influence of the local environment; and, as the network topology changes, regular updates of these indicators can help to continuously monitor and adjust the network configuration to ensure that it always has good anti-destruction performance; the use of highly anti-destruction transmission links can reduce the risk of data leakage caused by link failures or attacks. Even if the transmission link is damaged to a certain extent, the redundancy and recovery mechanism can ensure the continuous transmission and integrity of the data, effectively protecting the SaaS privacy data from unauthorized access and tampering, and ensuring the integrity and confidentiality of the SaaS privacy data;

[0028] (2) The SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine of the present invention uses the transmission link with the highest anti-destruction score to encrypt and transmit SaaS privacy data, and each network node in the anti-destruction link performs an anti-tampering check on the SaaS privacy data through the change of the configuration file of the fanotify component. Once a file is modified or tampered with, the fanotify component can immediately detect it, realize real-time monitoring and response, and forward the encrypted SaaS privacy data to the isolation area for virus feature matching, which can prevent the virus from spreading to the entire transmission link, ensure the security of SaaS privacy data during transmission, and avoid interference with normal business;

[0029] In summary, the SaaS privacy data transmission method of the end-to-end operation and maintenance software and hardware integrated machine of the present invention greatly improves the security of SaaS privacy data transmission of the end-to-end operation and maintenance software and hardware integrated machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine of the present invention;

[0031] Figure 2 Schematic diagram for finding a transmission link with the highest survivability. DETAILED DESCRIPTION

[0032] The technical solution of the present invention is further explained below in conjunction with the accompanying drawings.

[0033] like Figure 1The flowchart of the SaaS privacy data transmission method of the end-to-end operation and maintenance software and hardware integrated machine of the present invention is as follows:

[0034] Step S1: collect log information of SaaS privacy data transmitted in the end-to-end operation and maintenance hardware and software integrated machine, find out each transmission link of SaaS privacy data from the log information, and classify the transmission links of SaaS privacy data according to business types.

[0035] Step S2: for each type of SaaS private data transmission link, combine different types of attack strategies to find a transmission link with the highest survivability. Using a transmission link with high survivability can reduce the risk of data leakage caused by link failure or attack. Even if the transmission link is damaged to a certain extent, the redundancy and recovery mechanism can ensure the continuous transmission and integrity of the data, effectively protect the SaaS private data from unauthorized access and tampering, and ensure the integrity and confidentiality of the SaaS private data. Figure 2 , specifically including the following sub-steps:

[0036] Step S2.1, calculate the degree of each node, node betweenness and node clustering coefficient in the transmission link of SaaS privacy data of each business type under each attack strategy; the attack strategies involved in the present invention include: flooding attack security strategy, confusion attack strategy, anti-tampering attack strategy and large model anti-theft attack strategy, the node degree refers to the number of edges directly connected to the node, in a complex network, the larger the node degree, the closer the connection between the node and other nodes, and the more important its position and role in the network, and nodes with higher degrees may have higher anti-destruction ability because they have more connection paths; the node betweenness refers to the proportion of the number of paths passing through the node in all the shortest paths in the network, the larger the node betweenness, the stronger the control of the node in the network, and the important influence on the dissemination and circulation of information; the node clustering coefficient refers to the ratio of the actual number of edges between the neighbor nodes of the node to the possible number of edges, the larger the clustering coefficient, the closer the connection between the neighbor nodes of the node, and the greater the local influence of the node in the network, and the nodes with high clustering coefficients may have stronger local stability and anti-destruction ability because they form a close connection with the surrounding nodes. The three indicators of node degree, node betweenness and node clustering coefficient can be used to comprehensively evaluate the overall performance of the transmission link in the end-to-end operation and maintenance hardware all-in-one machine from different levels. Not only the importance of a single node is considered, but also its position in the network and the influence of the local environment are taken into account. Moreover, as the network topology changes, regular updates of these indicators can help continuously monitor and adjust the network configuration to ensure that it always has good anti-destruction performance.

[0037] Step S2.2, perform Min-Max normalization processing on each node degree, node betweenness and node clustering coefficient respectively;

[0038] Step S2.3: Weight all normalized node degrees, node betweenness, and node clustering coefficients in the transmission link of each piece of SaaS privacy data to obtain the invulnerability score of the transmission link of each piece of SaaS privacy data:

[0039]

[0040] Among them, S i represents the invulnerability score of the transmission link of the i-th SaaS private data, n represents the number of network nodes in the transmission link of the i-th SaaS private data, O j represents the normalized result of the node degree of the jth network node, α represents O j The weight coefficient, K j represents the normalized result of the node betweenness of the jth network node, and β represents K j The weight coefficient, F j represents the normalized result of the clustering coefficient of the jth network node, and γ represents F j The weight coefficient of .

[0041] Step S2.4: The transmission link with the highest invulnerability score in each service type is used as the transmission link of the service type.

[0042] Step S3: When transmitting SaaS private data in the end-to-end operation and maintenance hardware and software integrated machine, encrypt the SaaS private data and use the highest invulnerability transmission link corresponding to the same service type to transmit the SaaS private data, thereby achieving secure transmission of the SaaS private data; specifically, the following sub-steps are included:

[0043] Step S3.1, when transmitting SaaS privacy data in the end-to-end operation and maintenance hardware and software integrated machine, the SaaS privacy data is encrypted with Base64. Specifically, in the process of transmitting SaaS privacy data, Base64 is used to convert binary data into ASCII strings for transmission. Since Base64 encoding can convert binary data such as pictures, audio, and video into text data, its changes are more easily detected by the configuration file of the fanotify component;

[0044] Step S3.2, assigning the encrypted SaaS privacy data a socket identifier that records the transmission link with the highest invulnerability corresponding to the same service type;

[0045] Step S3.3: The encrypted SaaS privacy data is transmitted on the transmission link according to the Socket identifier, and an anti-tampering check is performed on the encrypted SaaS privacy data at each network node of the transmission link. If the check passes, the encrypted SaaS privacy data is transmitted, otherwise, the transmission is terminated.

[0046] The specific process of tracing the encrypted SaaS privacy data of the present invention is as follows: deploying fanotify components on all network nodes of the end-to-end operation and maintenance software integrated machine, when the encrypted SaaS privacy data is transmitted to a certain network node in the transmission link, since Base64 is used to convert binary data into ASCII character strings for text transmission, if the configuration file of the fanotify component changes, it may be that the transmission data is tampered with, or the node server is attacked and causes the configuration file to be tampered with. In order to timely perform fault tracing and anti-tampering inspection, the encrypted SaaS privacy data is forwarded to the isolation area for virus feature matching, and the configuration file of the changed fanotify component is matched for virus feature matching. If neither of them matches the virus feature, the transmission of the encrypted SaaS privacy data continues; otherwise, the transmission of the encrypted SaaS privacy data is terminated. Once a file is modified or tampered with during the transmission of SaaS privacy data, the fanotify component can detect it immediately, realize real-time monitoring and response, and forward the encrypted SaaS privacy data to the isolation area for virus feature matching, which can prevent the virus from spreading to the entire transmission link, ensure the security of SaaS privacy data during transmission, and avoid interference with normal business.

[0047] In a technical solution of the present invention, a computer-readable storage medium is also provided, storing a computer program, wherein the computer program enables a computer to execute the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine.

[0048] In a technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine is implemented.

[0049] In a technical solution of the present invention, a computer program product is also provided, including a computer program, which, when executed by a processor, implements the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine.

[0050] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0051] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0052] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A SaaS privacy data transmission method for an end-to-end operation and maintenance software and hardware integrated machine, characterized in that: The specific steps include: Step S1: collecting log information of SaaS privacy data transmitted in the end-to-end operation and maintenance hardware and software integrated machine, finding each transmission link of SaaS privacy data from the log information, and classifying the transmission links of SaaS privacy data according to business types; Step S2: for each type of SaaS privacy data transmission link, combine different types of attack strategies to find a transmission link with the highest anti-destruction capability; Step S3: When transmitting SaaS privacy data in the end-to-end operation and maintenance hardware and software integrated machine, the SaaS privacy data is encrypted, and the SaaS privacy data is transmitted using the transmission link with the highest invulnerability corresponding to the same business type.

2. According to claim 1, a SaaS privacy data transmission method for an end-to-end operation and maintenance hardware and software integrated machine is characterized in that: Step S2 includes the following sub-steps: Step S2.1, calculate the degree, betweenness and clustering coefficient of each node in the transmission link of SaaS privacy data of each business type under each attack strategy; Step S2.2, perform Min-Max normalization processing on each node degree, node betweenness and node clustering coefficient respectively; Step S2.3: weight all normalized node degrees, node betweenness, and node clustering coefficients in the transmission link of each piece of SaaS privacy data to obtain the invulnerability score of the transmission link of each piece of SaaS privacy data; Step S2.4: The transmission link with the highest invulnerability score in each service type is used as the transmission link of the service type.

3. The SaaS privacy data transmission method of an end-to-end operation and maintenance hardware and software integrated machine according to claim 2 is characterized in that: The attack strategies in step S2 include: flooding attack strategy, confusion attack strategy, tampering attack strategy and large model stealing attack strategy.

4. The SaaS privacy data transmission method of an end-to-end operation and maintenance hardware and software integrated machine according to claim 2 is characterized in that: The calculation process of the invulnerability score of each SaaS privacy data transmission link in step S2.3 is as follows: Among them, S i represents the invulnerability score of the transmission link of the i-th SaaS private data, n represents the number of network nodes in the transmission link of the i-th SaaS private data, O j represents the normalized result of the node degree of the jth network node, α represents O j The weight coefficient, K j represents the normalized result of the node betweenness of the jth network node, and β represents K j The weight coefficient, F j represents the normalized result of the clustering coefficient of the jth network node, and γ represents F j The weight coefficient of .

5. The SaaS privacy data transmission method of an end-to-end operation and maintenance hardware and software integrated machine according to claim 4 is characterized in that: Step S3 includes the following sub-steps: Step S3.1, when transmitting SaaS privacy data in the end-to-end operation and maintenance hardware and software integrated machine, encrypt the SaaS privacy data with Base64; Step S3.2, assigning the encrypted SaaS privacy data a socket identifier that records the transmission link with the highest invulnerability corresponding to the same service type; Step S3.3: The encrypted SaaS privacy data is transmitted on the transmission link according to the Socket identifier, and an anti-tampering check is performed on the encrypted SaaS privacy data at each network node of the transmission link. If the check passes, the encrypted SaaS privacy data is transmitted, otherwise, the transmission is terminated.

6. The SaaS privacy data transmission method of an end-to-end operation and maintenance hardware and software integrated machine according to claim 5 is characterized in that: The specific process of performing anti-tampering check on the encrypted SaaS privacy data in step S3.3 is as follows: deploy the fanotify component on all network nodes of the end-to-end operation and maintenance software integrated machine. When the encrypted SaaS privacy data is transmitted to a certain network node in the transmission link, if the configuration file of the fanotify component changes, the encrypted SaaS privacy data will be forwarded to the isolation area for virus feature matching, and the configuration file of the changed fanotify component will be matched for virus features. If neither of them matches the virus feature, the transmission of the encrypted SaaS privacy data will continue; otherwise, the transmission of the encrypted SaaS privacy data will be terminated.

7. A computer-readable storage medium storing a computer program, characterized in that: include: The computer program enables the computer to execute the computer program. When the computer program is executed by the processor, it implements the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine as described in any one of claims 1-6.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine as described in any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by the processor, the SaaS privacy data transmission method of the end-to-end operation and maintenance hardware and software integrated machine as described in any one of claims 1-6 is implemented.

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