Information processing method and system for unmanned aerial vehicle chip encryption system

Abstract

This invention provides an information processing method and system for an unmanned aerial vehicle (UAV) chip encryption system. The method involves acquiring first template system encryption control data for a system anomaly analysis network; loading this first template system encryption control data into scrambling control features to obtain template scrambling loading data; restoring the scrambling loading data to obtain second template system encryption control data for the system anomaly analysis network; and optimizing the system anomaly analysis network with initialized weight parameters based on the first and second template system encryption control data to obtain an optimized system anomaly analysis network. Through this technical solution, high-precision second template system encryption control data is generated. Therefore, training the system anomaly analysis network with this high-precision template system encryption control data can enhance the analysis accuracy of the system anomaly analysis network.

Description

Technical Field

[0001] This invention relates to the field of AI technology, and more specifically, to an information processing method and system for a drone chip encryption system. Background Technology

[0002] Encryption systems are a common security technology that transforms data and information to make them unreadable, thereby protecting the security of personal data and information. The main purpose of encryption systems is to protect confidentiality and securely transmit data and information to ensure the confidentiality of communication interactions. However, encryption systems cannot solve all security problems. When anomalies occur during system monitoring, how to accurately analyze the control data of the encryption system, find the anomaly points, and thus improve the accuracy of subsequent anomaly analysis of the encryption system is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide an information processing method and system for an unmanned aerial vehicle (UAV) chip encryption system. The method involves: acquiring first template system encryption control data for a system anomaly analysis network; loading the first template system encryption control data into scrambling control features to obtain template scrambling loading data; restoring the template scrambling loading data to obtain second template system encryption control data for the system anomaly analysis network; and optimizing the system anomaly analysis network with initialized weight parameters based on the first and second template system encryption control data to obtain an optimized system anomaly analysis network. Through this technical solution, high-precision second template system encryption control data is generated. Therefore, training the system anomaly analysis network with high-precision template system encryption control data can enhance the analysis accuracy of the system anomaly analysis network.

[0004] According to one aspect of the present invention, an information processing method and system for an unmanned aerial vehicle (UAV) chip encryption system are provided, the method comprising:

[0005] Obtain the first template system encryption control data of the system anomaly analysis network, wherein the first template system encryption control data carries the corresponding anomaly encryption operation tag data;

[0006] The first template system encrypted control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data;

[0007] The scrambled loaded data of the template is scrambled and restored to generate the second template system encrypted control data of the system anomaly analysis network;

[0008] Based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, the system anomaly analysis network with initialized weight parameters is optimized to generate an optimized system anomaly analysis network.

[0009] In an alternative implementation, after acquiring the first template system encryption control data of the system anomaly analysis network, the method further includes:

[0010] The first template system encrypted control data of the system anomaly analysis network is subjected to regularized feature encoding to generate the first template system encrypted control data after regularized feature encoding;

[0011] The first template system encryption control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data, including:

[0012] The first template system encrypted control data, after being encoded with the regularized features, is loaded into the scrambling control features to generate template scrambling loading data.

[0013] In an alternative implementation, the first template system encryption control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data, including:

[0014] Based on heuristic search, the first template system encrypted control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data.

[0015] In an alternative implementation, the step of scrambling and restoring the template scrambled loaded data to generate the second template system encrypted control data for the system anomaly analysis network includes:

[0016] Obtain the scrambling feature data loaded into the template scrambling loading data;

[0017] The template scrambling loading data and the scrambling feature data loaded into the template scrambling loading data are input into the AI ​​neural network to generate the second template system encryption control data of the system anomaly analysis network.

[0018] In an alternative implementation, the step of optimizing the system anomaly analysis network based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate an optimized system anomaly analysis network includes:

[0019] Based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, active learning is performed to generate basic network weight information;

[0020] Based on the basic network weight information, the system anomaly analysis network with initialized weight parameters is optimized to generate an optimized system anomaly analysis network.

[0021] In an alternative implementation, the step of actively learning based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate basic network weight information includes:

[0022] Based on the active learning algorithm, feature extraction is performed on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate basic network weight information.

[0023] In an alternative implementation, after optimizing the system anomaly analysis network with initialized weight parameters based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate an optimized system anomaly analysis network, the method further includes:

[0024] Obtain the encrypted control data of the system to be analyzed;

[0025] The system encryption control data to be analyzed is loaded into the optimized system anomaly analysis network to generate weight values ​​for at least one anomaly encryption operation.

[0026] The abnormal encryption operation analysis data is determined based on the weight value of each abnormal encryption operation.

[0027] According to another aspect of the present invention, an information processing method and system for an unmanned aerial vehicle (UAV) chip encryption system are provided, the system comprising:

[0028] The acquisition unit is used to acquire the first template system encryption control data of the system anomaly analysis network, wherein the first template system encryption control data carries the corresponding anomaly encryption operation tag data.

[0029] The first generation unit is used to load the first template system encryption control data of the system anomaly analysis network into the scrambling control feature to generate template scrambling loading data.

[0030] The second generation unit is used to scramble and restore the template scrambled loaded data to generate the second template system encrypted control data of the system anomaly analysis network.

[0031] The optimization unit is used to optimize the system anomaly analysis network with initialized weight parameters based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, and generate an optimized system anomaly analysis network.

[0032] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the training method of the system anomaly analysis network described in any of the preceding claims.

[0033] According to another aspect of the present invention, a readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, can perform the steps of the above-described information processing method for an unmanned aerial vehicle chip encryption system.

[0034] To make the above-mentioned objects, features and advantages of the embodiments of the present invention more apparent and understandable, a detailed description will be given below in conjunction with the embodiments and the accompanying drawings. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A schematic diagram of the components of a server provided in an embodiment of the present invention is shown;

[0037] Figure 2 This invention provides a flowchart illustrating the information processing method and system for an unmanned aerial vehicle (UAV) chip encryption system according to an embodiment of the present invention.

[0038] Figure 3 The diagram illustrates a functional block diagram of an information processing system for a drone chip encryption system, as provided in an embodiment of the present invention. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] The terms “first,” “second,” “third,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0041] Figure 1 An exemplary component diagram of server 100 is shown. Server 100 may include one or more processors 104, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Server 100 may also include any storage medium 106 for storing any kind of information such as code, settings, data, etc. Without limitation, for example, storage medium 106 may include any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any storage medium can use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of server 100. In one case, server 100 may perform any operation of the associated instructions when processor 104 executes associated instructions stored in any storage medium or combination of storage media. Server 100 also includes one or more drive units 108 for interacting with any storage medium, such as hard disk drive units, optical disk drive units, etc.

[0042] Server 100 also includes input / output 110 (I / O) for receiving various inputs (via input unit 112) and providing various outputs (via output unit 114). A specific output mechanism may include a presentation device 116 and an associated graphical user interface (GUI) 118. Server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the components described above together.

[0043] The communication unit 122 can be implemented in any manner, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name server 100, etc., governed by any protocol or combination of protocols.

[0044] Figure 2 This invention illustrates a flowchart of an information processing method and system for a drone chip encryption system, provided by an embodiment of the present invention. The information processing method and system for a drone chip encryption system can be derived from... Figure 1 The server 100 shown in the figure executes the following detailed steps of the information processing method and system for the drone chip encryption system.

[0045] Step S110: Obtain the first template system encryption control data of the system anomaly analysis network. The first template system encryption control data carries the corresponding anomaly encryption operation tag data.

[0046] Step S120: Load the first template system encryption control data of the system anomaly analysis network into the scrambling control feature to generate template scrambling loading data;

[0047] Step S130: The template scrambled loaded data is scrambled and restored to generate the second template system encrypted control data of the system anomaly analysis network;

[0048] Step S140: Based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, the system anomaly analysis network with initialized weight parameters is optimized to generate the optimized system anomaly analysis network.

[0049] Based on the above steps, this embodiment obtains the first template system encryption control data of the system anomaly analysis network; loads the first template system encryption control data of the system anomaly analysis network into the scrambling control feature to obtain template scrambling loading data; restores the template scrambling loading data to obtain the second template system encryption control data of the system anomaly analysis network; optimizes the system anomaly analysis network with initialized weight parameters based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to obtain the optimized system anomaly analysis network. Through the above technical solution, high-precision second template system encryption control data is generated. Therefore, training the system anomaly analysis network with high-precision template system encryption control data can enhance the analysis accuracy of the system anomaly analysis network.

[0050] In an alternative implementation, after acquiring the first template system encryption control data of the system anomaly analysis network, the method further includes:

[0051] The first template system encrypted control data of the system anomaly analysis network is subjected to regularized feature encoding to generate the first template system encrypted control data after regularized feature encoding;

[0052] The first template system encryption control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data, including:

[0053] The first template system encrypted control data, after being encoded with the regularized features, is loaded into the scrambling control features to generate template scrambling loading data.

[0054] In an alternative implementation, the first template system encryption control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data, including:

[0055] Based on heuristic search, the first template system encrypted control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data.

[0056] In an alternative implementation, the step of scrambling and restoring the template scrambled loaded data to generate the second template system encrypted control data for the system anomaly analysis network includes:

[0057] Obtain the scrambling feature data loaded into the template scrambling loading data;

[0058] The template scrambling loading data and the scrambling feature data loaded into the template scrambling loading data are input into the AI ​​neural network to generate the second template system encryption control data of the system anomaly analysis network.

[0059] In an alternative implementation, the step of optimizing the system anomaly analysis network based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate an optimized system anomaly analysis network includes:

[0060] Based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, active learning is performed to generate basic network weight information;

[0061] Based on the basic network weight information, the system anomaly analysis network with initialized weight parameters is optimized to generate an optimized system anomaly analysis network.

[0062] In an alternative implementation, the step of actively learning based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate basic network weight information includes:

[0063] Based on the active learning algorithm, feature extraction is performed on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate basic network weight information.

[0064] In an alternative implementation, after optimizing the system anomaly analysis network with initialized weight parameters based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate an optimized system anomaly analysis network, the method further includes:

[0065] Obtain the encrypted control data of the system to be analyzed;

[0066] The system encryption control data to be analyzed is loaded into the optimized system anomaly analysis network to generate weight values ​​for at least one anomaly encryption operation.

[0067] The abnormal encryption operation analysis data is determined based on the weight value of each abnormal encryption operation.

[0068] Figure 3A functional block diagram of an information processing system 200 for an unmanned aerial vehicle (UAV) chip encryption system, according to an embodiment of the present invention, is shown. The functions implemented by this information processing system 200 for an UAV chip encryption system correspond to the steps performed by the above-described method. This information processing system 200 for an UAV chip encryption system can be understood as the aforementioned server 100, or the processor of server 100, or it can be understood as a component independent of server 100 or the processor, but implementing the functions of the present invention under the control of server 100, such as... Figure 3 As shown below, the functions of each functional module of the information processing system 200 used for UAV chip encryption system will be described in detail.

[0069] The acquisition unit 210 is used to acquire the first template system encryption control data of the system anomaly analysis network, wherein the first template system encryption control data carries the corresponding anomaly encryption operation tag data.

[0070] The first generation unit 220 is used to load the first template system encryption control data of the system anomaly analysis network into the scrambling control feature to generate template scrambling loading data.

[0071] The second generation unit 230 is used to scramble and restore the template scrambled loaded data to generate the second template system encrypted control data of the system anomaly analysis network.

[0072] The optimization unit 240 is used to optimize the system anomaly analysis network with initialized weight parameters based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, and generate an optimized system anomaly analysis network.

[0073] In an alternative implementation, the acquisition unit 210 is further configured to:

[0074] The first template system encrypted control data of the system anomaly analysis network is subjected to regularized feature encoding to generate the first template system encrypted control data after regularized feature encoding;

[0075] The first template system encryption control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data, including:

[0076] The first template system encrypted control data, after being encoded with the regularized features, is loaded into the scrambling control features to generate template scrambling loading data.

[0077] In an alternative implementation, the acquisition unit 210 is further configured to:

[0078] Based on heuristic search, the first template system encrypted control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data.

[0079] In an alternative implementation, the second generating unit 230 is further configured to:

[0080] Obtain the scrambling feature data loaded into the template scrambling loading data;

[0081] The template scrambling loading data and the scrambling feature data loaded into the template scrambling loading data are input into the AI ​​neural network to generate the second template system encryption control data of the system anomaly analysis network.

[0082] In an alternative implementation, the optimization unit 240 is further configured to:

[0083] Based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, active learning is performed to generate basic network weight information;

[0084] Based on the basic network weight information, the system anomaly analysis network with initialized weight parameters is optimized to generate an optimized system anomaly analysis network.

[0085] In an alternative implementation, the optimization unit 240 is further configured to:

[0086] Based on the active learning algorithm, feature extraction is performed on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate basic network weight information.

[0087] In an alternative implementation, the acquisition unit 210 is further configured to:

[0088] Obtain the encrypted control data of the system to be analyzed;

[0089] The system encryption control data to be analyzed is loaded into the optimized system anomaly analysis network to generate weight values ​​for at least one anomaly encryption operation.

[0090] The abnormal encryption operation analysis data is determined based on the weight value of each abnormal encryption operation.

[0091] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0092] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

Claims

1. An information processing method for a UAV chip encryption system, characterized in that, The method includes: Obtain the first template system encryption control data of the system anomaly analysis network, wherein the first template system encryption control data carries the corresponding anomaly encryption operation tag data; The first template system encrypted control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data; The scrambled loaded data of the template is scrambled and restored to generate the second template system encrypted control data of the system anomaly analysis network; Based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, the system anomaly analysis network with initialized weight parameters is optimized to generate an optimized system anomaly analysis network. After obtaining the first template system encrypted control data of the system anomaly analysis network, the following is also included: The first template system encrypted control data of the system anomaly analysis network is subjected to regularized feature encoding to generate the first template system encrypted control data after regularized feature encoding; The first template system encryption control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data, including: The first template system encrypted control data after the regularization feature encoding is loaded into the scrambling control feature to generate template scrambling loading data; The first template system encryption control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data, including: Based on heuristic search, the first template system encrypted control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data. The step of scrambling and restoring the template-scrambled loaded data to generate the second template system encrypted control data for the system anomaly analysis network includes: Obtain the scrambling feature data loaded into the template scrambling loading data; The template scrambling loading data and the scrambling feature data loaded into the template scrambling loading data are input into the AI ​​neural network to generate the second template system encryption control data of the system anomaly analysis network.

2. The information processing method for a UAV chip encryption system according to claim 1, characterized in that, The process of optimizing the system anomaly analysis network based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate an optimized system anomaly analysis network includes: Based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, active learning is performed to generate basic network weight information; Based on the basic network weight information, the system anomaly analysis network with initialized weight parameters is optimized to generate an optimized system anomaly analysis network.

3. The information processing method for a UAV chip encryption system according to claim 1, characterized in that, The process involves active learning based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate basic network weight information, including: Based on the active learning algorithm, feature extraction is performed on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate basic network weight information.

4. The information processing method for a UAV chip encryption system according to claim 1, characterized in that, After optimizing the system anomaly analysis network based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network to generate the optimized system anomaly analysis network, the process further includes: Obtain the encrypted control data of the system to be analyzed; The system encryption control data to be analyzed is loaded into the optimized system anomaly analysis network to generate weight values ​​for at least one anomaly encryption operation. The abnormal encryption operation analysis data is determined based on the weight value of each abnormal encryption operation.

5. An information processing system for an unmanned aerial vehicle (UAV) chip encryption system, characterized in that, include: The acquisition unit is used to acquire the first template system encryption control data of the system anomaly analysis network, wherein the first template system encryption control data carries the corresponding anomaly encryption operation tag data. The first generation unit is used to load the first template system encryption control data of the system anomaly analysis network into the scrambling control feature to generate template scrambling loading data. The second generation unit is used to scramble and restore the template scrambled loaded data to generate the second template system encrypted control data of the system anomaly analysis network; including: Obtain the scrambling feature data loaded into the template scrambling loading data; The template scrambling loading data and the scrambling feature data loaded into the template scrambling loading data are input into the AI ​​neural network to generate the second template system encryption control data of the system anomaly analysis network. The optimization unit is used to optimize the system anomaly analysis network with initialized weight parameters based on the first template system encryption control data and the second template system encryption control data of the system anomaly analysis network, and generate an optimized system anomaly analysis network. After obtaining the first template system encrypted control data of the system anomaly analysis network, the following is also included: The first template system encrypted control data of the system anomaly analysis network is subjected to regularized feature encoding to generate the first template system encrypted control data after regularized feature encoding; The first template system encryption control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data, including: The first template system encrypted control data after the regularization feature encoding is loaded into the scrambling control feature to generate template scrambling loading data; The first template system encryption control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data, including: Based on heuristic search, the first template system encrypted control data of the system anomaly analysis network is loaded into the scrambling control feature to generate template scrambling loading data. The step of scrambling and restoring the template-scrambled loaded data to generate the second template system encrypted control data for the system anomaly analysis network includes: Obtain the scrambling feature data loaded into the template scrambling loading data; The template scrambling loading data and the scrambling feature data loaded into the template scrambling loading data are input into the AI ​​neural network to generate the second template system encryption control data of the system anomaly analysis network.

6. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the training method of the system anomaly analysis network according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the training method of the system anomaly analysis network according to any one of claims 1-4.

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