A cloud mobile phone full-dimension risk control environment unified simulation and detection adaptation method and system

Abstract

The application discloses a cloud mobile phone full-dimension risk control environment unified simulation and detection adaptation method and system, comprising the following steps: constructing a standardized risk control template library covering six dimensions of hardware parameters, sensor data, network environment, system identification, container running environment and Root / mask risk for multi-vendor heterogeneous cloud mobile phones; performing full-dimension environment scanning on all kinds of heterogeneous cloud mobile phones accessed, comparing the collected original characteristic data with the template library dimension by dimension, identifying differentiated characteristics and risk items; performing unified simulation and characteristic flattening processing and deep purification processing on each dimension according to the identification result; then performing full-dimension high-risk environment scanning and closed-loop repair; and finally outputting a normalized risk control environment and dynamically and continuously adapting. The application realizes six-dimensional full-stack risk control adaptation, solves the technical problem of differentiated heterogeneous devices, and realizes unified adaptation and long-term stable maintenance of the full-dimension risk control environment.

Description

Technical Field

[0001] This invention belongs to the field of cloud phone virtualization and device risk control environment simulation technology, and in particular relates to a unified simulation and detection adaptation method and system for cloud phone full-dimensional risk control environment. Background Technology

[0002] The current multi-vendor heterogeneous cloud phone system suffers from severe industry pain points, including fragmented risk control environments, inconsistent security baselines, and inconsistent risk detection standards. Different vendors' cloud phones, based on different virtualization architectures, underlying drivers, container solutions, and system customization strategies, exhibit significant differences in exposed hardware characteristics, sensor data, network parameters, system identifiers, and operating environment characteristics. Furthermore, the varying degrees of exposure to root privileges, mask plugins, and container sandbox environments among different cloud phones make them highly susceptible to being identified as high-risk devices, virtual machine devices, or batch devices in a cluster by business risk control systems, leading to issues such as service throttling, blocking, and banning.

[0003] Existing traditional technical solutions have significant shortcomings, mainly in the following aspects: 1. Single-dimensional risk control adaptation: Traditional solutions only masquerade simple device parameters, which cannot simultaneously cover the six core risk control dimensions of hardware, sensors, network, system identification, container environment, and root risk. The environment simulation is low and it is easy to be penetrated and identified by risk control. 2. Inconsistent risk control environment for heterogeneous devices: The native risk control baselines of Baidu, Huawei, and self-built cloud phones differ greatly. Some devices have virtualization characteristics, container residual identifiers, and underlying framework characteristics, making it impossible to form a unified security disguise environment. As a result, the risk control performance of cross-device business is inconsistent. 3. Lack of systematic Root / mask detection and smoothing mechanism: Traditional solutions lack the ability to deeply detect and restore the environment for Root privileges, mask plugins, hidden modules, and injection processes. High-risk environments cannot be uniformly repaired, and the security baseline of devices is inconsistent. 4. Severe exposure of container environment characteristics: Commercial cloud phones generally retain container mounting characteristics, virtualization processes, and cloud device-specific identifiers, which cannot simulate the pure operating environment of a real physical machine and are easily identified as virtual devices by risk control. 5. Lack of unified risk control adaptation standards: The risk control adaptation logic of different manufacturers' equipment is independent and the rules are scattered. There is no global normalization and smoothing strategy, resulting in high operation and maintenance costs, poor risk control stability, and inability to guarantee consistency of batch equipment.

[0004] In summary, the current industry lacks a full-stack risk control adaptation system that covers all risk control dimensions, adapts to heterogeneous devices from multiple vendors, and enables unified simulation, unified detection, and unified smoothing. This represents a technological gap in the current cloud phone security risk control field and has extremely high innovation and practical value. Summary of the Invention

[0005] To address the technical pain points of existing multi-vendor heterogeneous cloud phone risk control environments, such as fragmentation, incomplete security dimension coverage, inaccurate detection of root / high-risk environments, difficulty in completely eliminating virtualization characteristics, and inconsistent risk control performance across devices, this invention aims to provide a unified simulation and detection adaptation method and system for cloud phone full-dimensional risk control environments.

[0006] The technical solution adopted by this invention to solve its technical problem is: In one aspect, a unified simulation and detection adaptation method for cloud mobile phone full-dimensional risk control environment is provided, including the following steps: S100: A standardized risk control template library is built for heterogeneous cloud phones from multiple vendors as a benchmark for unified output of the full-dimensional risk control environment. The standardized risk control template library includes six dimensions: hardware parameters, sensor data, network environment, system identification, container operating environment, and root / mask risk. S200: Performs a full-dimensional environmental scan on all types of heterogeneous cloud phones connected to the network, collects native feature data and risk exposure points of each device in each risk control dimension, compares the collected native feature data with the standardized risk control template library dimension by dimension, and identifies the differentiated features and risk items of each device. S300: Based on the identification results of differentiated features and risk items, unified simulation and feature smoothing are performed on the hardware parameter dimension, sensor data dimension, network environment dimension, and system identification dimension, respectively, and deep purification is performed on the container operating environment dimension to eliminate the original differences and virtualization residual features of each device in each risk control dimension. S400: Performs a full-dimensional high-risk environment scan on each device after feature smoothing, accurately identifies root privilege enabled status, masked modules, hidden plugins and injected process risk items, automatically performs environment repair and risk feature removal on the identified risk items, and performs a secondary environment verification on the repair results to confirm that the risk has been eliminated. S500: After processing through the above steps, each device outputs a normalized, highly realistic device risk control environment according to the unified standard of the standardized risk control template library. During the operation of the device, it monitors the changes in the environment of each risk control dimension in real time and dynamically refreshes the risk control features to maintain the continuous stability and uniformity of the risk control environment.

[0007] Preferably, the unified simulation and feature smoothing processing of hardware parameters in S300 includes: Based on the sub-templates corresponding to the hardware parameter dimension in the standardized risk control template library, a standardized hardware parameter template library is established. The hardware parameters of heterogeneous cloud phones from multiple manufacturers are standardized and dynamically replaced according to the template library. The hardware parameters include IMEI, MEID, device serial number, manufacturer model, battery parameters, screen parameters, storage parameters, and CPU / GPU parameters. Furthermore, abnormal hardware fields, virtualization identifiers, and cloud device-specific parameters native to each manufacturer's devices are cleaned and replaced in batches to achieve normalized and realistic output of hardware risk control features for all devices.

[0008] Preferably, the unified simulation and feature smoothing processing of sensor data dimensions in S300 includes: Standard data from the sensor dimensions in the standardized risk control template library is used as the simulation benchmark to build a full-dimensional sensor simulation engine. This engine uniformly simulates real-time sensor data from gravity sensors, gyroscopes, magnetometers, distance sensors, light sensors, and attitude sensors. Natural dynamic noise and fluctuation data are added, and the sampling frequency, data accuracy, and variation patterns of sensors from multiple devices are unified. This smooths out the differences in risk control characteristics between sensors from heterogeneous devices and achieves consistent adaptation of risk control characteristics across sensors from different devices.

[0009] Preferably, the unified simulation and feature smoothing processing of the network environment dimension in S300 includes: The system utilizes standardized network-level baselines from a standardized risk control template library, covering network risk control dimensions such as IP address, network type, DNS link, gateway parameters, network latency, routing characteristics, TLS fingerprints, and HTTP / HTTPS request characteristics. Addressing the network architecture differences of cloud phones from various vendors, the system unifies the network environment baseline, cleans cloud data center IP characteristics, identifies devices on the same network segment, and identifies virtualized networks, simulating real mobile or broadband network environments to ensure consistent network risk control behavior across multiple devices.

[0010] Preferably, the unified simulation and feature smoothing processing of the system identifier dimension in S300 includes: By calling upon the standard features of the system identifier dimension in the standardized risk control template library, a comprehensive system identifier management system is constructed, which uniformly adapts to Android version, system compilation fingerprint, system attributes, device build information, permission list, system services, process name and package management features; batch clears the cloud device identifier, virtualization system field and vendor-customized features that come with cloud phones from various manufacturers, and uniformly outputs standardized physical machine system environment features to avoid system-level virtualization risk control identification.

[0011] Preferably, the deep purification process for the container operating environment in S300 includes: Based on the clean environment standard of the container runtime environment dimension in the standardized risk control template library, the system detects and hides the container mount nodes, virtualization processes, cloud service resident processes, and container-specific environment variables of cloud phones; it also masks cgroup, namespace, and docker feature identifiers, uniformly simulates a clean physical machine runtime environment, eliminates differences in container environments from multiple vendors, and achieves complete unification of container environment risk control features.

[0012] Preferably, the unified detection and closed-loop repair of high-risk Root / mask operating environments in S400 includes: Using the security baseline in the standardized risk control template library as the basis for risk assessment, a unified risk scan is performed on heterogeneous cloud phones from multiple manufacturers. The detection targets of the risk scan include root permission enabled status, Magisk module, hidden plugins, SU permission binary files, injected processes, tampered system files, and high-risk hidden environments. Based on the risk scan results, the system automatically performs environment repair, permission revocation, file restoration, process cleanup, and risk feature removal operations on the detected risk items.

[0013] Preferably, in S400, performing a secondary environmental verification on the repair results to confirm risk elimination includes: After repair, perform a full-dimensional high-risk environment scan on each device and compare the scan results with the safety baseline in the standardized risk control template library. If the comparison is consistent, the risk is confirmed to be eliminated and the process proceeds to the normalized risk control environment output step. If the comparison is inconsistent, repeat the environment repair and risk feature removal operation until the comparison is consistent with the safety baseline.

[0014] Preferably, the S500 monitors changes in the risk control environment across various dimensions in real time and dynamically updates risk control features to maintain the continuous stability and consistency of the risk control environment, including: During operation, the system monitors environmental changes across all risk control dimensions in real time, dynamically updates risk control features based on the monitored environmental changes, and re-triggers the execution of S200's full-dimensional environmental scan, dimension-by-dimensional comparison and differential feature identification, S300's multi-dimensional unified simulation and feature smoothing processing, and S400's full-dimensional high-risk environmental scan, risk identification and environmental remediation when environmental changes are detected, in order to maintain the continuous stability and uniformity of the risk control environment.

[0015] In another aspect, a unified simulation and detection adaptation system for cloud-based mobile phone risk control environments across all dimensions is provided, including: The standardized risk control template library construction module is used to build a standardized risk control template library for heterogeneous cloud phones from multiple vendors as a benchmark for unified output of the full-dimensional risk control environment. The standardized risk control template library includes six dimensions: hardware parameters, sensor data, network environment, system identification, container operating environment, and root / mask risk. The full-dimensional environment scanning and differential analysis module is used to perform full-dimensional environment scanning on various heterogeneous cloud phones connected to the network, collect native feature data and risk exposure points of each device in each risk control dimension, and compare the collected native feature data with the standardized risk control template library dimension by dimension to identify the differential features and risk items of each device. The multi-dimensional heterogeneous feature unified simulation and smoothing module is used to perform unified simulation and feature smoothing processing on the hardware parameter dimension, sensor data dimension, network environment dimension, and system identification dimension according to the identification results of differentiated features and risk items, and to perform deep purification processing on the container operating environment dimension to eliminate the original differences and virtualization residual features of each device in each risk control dimension. The high-risk operating environment detection and closed-loop repair module is used to perform a full-dimensional high-risk environment scan on each device after feature smoothing. It accurately identifies risk items such as root permission enabled status, masked modules, hidden plugins and injected processes. It automatically performs environment repair and risk feature removal on the identified risk items, and performs a secondary environment verification on the repair results to confirm that the risk has been eliminated. The normalized risk control environment output and dynamic continuous adaptation module is used to output a normalized and highly realistic equipment risk control environment for each device after the above steps according to the unified standard of the standardized risk control template library. During the operation of the equipment, it monitors the changes of each risk control dimension environment in real time and dynamically refreshes the risk control features to maintain the continuous stability and uniformity of the risk control environment.

[0016] This invention addresses the differences in architecture among heterogeneous cloud phones from multiple vendors by constructing a full-stack risk control adaptation system covering six dimensions: hardware parameters, sensor data, network environment, system identifiers, container operating environment, and root / mask risks. This system enables unified simulation of risk control features for heterogeneous devices, unified detection of high-risk environments, and unified smoothing of virtualization features. As a result, heterogeneous cloud phones from multiple vendors output a consistent, clean, and realistic physical machine-level risk control environment, completely resolving the issue of cross-device risk control differences and improving business security, stability, and versatility. Attached Figure Description

[0017] Figure 1 This is a flowchart of a unified simulation and detection adaptation method for a cloud phone's all-dimensional risk control environment, as described in one embodiment of the present invention. Figure 2 This is the overall architecture diagram of the cloud phone all-dimensional risk control environment unified simulation and detection adaptation system of the present invention; Figure 3 This is a logic diagram for Root / Mask Depth Detection and Repair in this invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0019] This invention provides a unified simulation and detection adaptation method and system for the full-dimensional risk control environment of cloud phones, which is applicable to standardized risk control camouflage, security environment unification, unified identification of risk environment, and business security operation scenarios of multi-vendor heterogeneous cloud phone clusters such as self-built cloud phones, cloud phones from the first type of vendor, and cloud phones from the second type of vendor.

[0020] This invention uses a standardized risk control template library covering six dimensions—hardware parameters, sensor data, network environment, system identification, container operating environment, and root / mask risks—as a unified benchmark. It performs a full-dimensional environmental scan and difference analysis on various heterogeneous cloud phones. Based on the identification results of differentiated features and risk items, it performs unified simulation and feature smoothing or deep purification processing on each dimension. Then, it performs high-risk environment detection and closed-loop repair on the processed devices, ultimately outputting a normalized, highly realistic device risk control environment, which continuously and dynamically adapts during operation. The overall flowchart is as follows: Figure 1 As shown, the overall plan is as follows.

[0021] (I) S100: A standardized risk control template library is built for heterogeneous cloud phones from multiple vendors as a benchmark for unified output of the full-dimensional risk control environment. The standardized risk control template library includes six dimensions: hardware parameters, sensor data, network environment, system identification, container operating environment and root / mask risk.

[0022] Specifically, the overall architecture diagram of the six-dimensional full-stack risk control unified simulation of this invention is as follows: Figure 2 As shown, this invention adopts a layered architecture with unified global baseline management, parallel adaptation of six-dimensional modules, and unified access to heterogeneous devices. Through six core modules, it addresses the differences in various risk control dimensions, ultimately outputting a unified, standardized, and highly realistic device risk control environment. In this invention, multi-vendor heterogeneous cloud phones include, but are not limited to, self-built cloud phones, first-category vendor cloud phones (in this embodiment, Baidu Cloud Phone), and second-category vendor cloud phones (in this embodiment, Huawei Cloud Phone). Because different vendor cloud phones are based on different virtualization architectures, underlying drivers, container solutions, and system customization strategies, their exposed characteristics differ greatly. Therefore, it is necessary to pre-establish a standardized risk control template library as a unified benchmark and target for all subsequent adaptation operations.

[0023] The standardized risk control template library is uniformly stored and managed by the global risk control baseline management module. It includes standard data in six dimensions: hardware parameters, sensor data, network environment, system identifier, container operating environment, and root / mask risk, serving as the benchmark for unified output of the all-dimensional risk control environment.

[0024] Furthermore, the hardware parameter dimension describes the set of identifiable features of the underlying hardware of cloud phones, including IMEI, MEID, device serial number, manufacturer model, CPU / GPU architecture, battery and screen parameters, etc. Its core function is to uniformly model the hardware differences of cloud phones from different manufacturers, replace and map them through standardized templates, eliminate the exposure of non-physical machine features of virtualized devices and cloud data center devices, and output a consistent hardware fingerprint baseline.

[0025] The sensor data dimension describes the dynamic data characteristics of various sensors during equipment operation, including accelerometers, gyroscopes, magnetometers, light sensors, distance sensors, and attitude sensors. Its core function is to perform unified simulation and noise modeling of the sampling frequency, data distribution, and variation patterns of different device sensors, so that the sensor data presents natural and continuous variation characteristics, eliminates fixed patterns or abnormal synchronization characteristics, and improves environmental consistency and realism.

[0026] The network environment dimension describes the network links and communication characteristics of the device, including IP address attribution, network type, DNS path, gateway information, latency characteristics, routing path, and TLS / HTTP fingerprints. Its core function is to unify the network exit and link characteristics of different cloud phones, clean up cloud data center IPs, batch characteristics of the same network segment, and virtual network identifiers, simulate the behavior of real mobile networks or home broadband networks, and reduce the risk of cluster association identification.

[0027] The system identifier dimension describes the operating system and runtime environment identifier information, including Android version, build fingerprint, system attributes, permission list, system services, process naming, and package management information. Its core function is to standardize and rewrite custom fields from different vendors' systems, masking cloud device identifiers and virtualization system characteristics, and enabling the system layer to output a consistent system profile that closely resembles that of a real physical device.

[0028] The container runtime environment dimension describes the underlying containerized runtime characteristics of cloud phones, including cgroups, namespaces, Docker identifiers, mounted nodes, virtualization processes, persistent cloud service processes, and environment variables. Its core function is to deeply clean and hide virtualization remnants at the container layer, eliminate the exposure of cloud platform characteristics, and make the runtime environment present a "physical machine-like" pure execution state, thereby reducing the probability of containerization identification.

[0029] The Root / Magisk risk dimension describes system security and high-risk operational states, including root privileges, Magisk mask modules, SU binaries, hidden plugins, injected processes, and system tampering behaviors. Its core function is to uniformly detect, identify, and remediate high-risk permissions and covert modification behaviors. It eliminates high-risk characteristics through methods such as permission revocation, file restoration, and process cleanup, achieving a unified output of the security baseline.

[0030] Figure 2 This paper illustrates the overall architecture of the cloud phone full-dimensional risk control environment unified simulation and detection adaptation system of the present invention. The system adopts a layered modular design, comprising, from top to bottom, a global risk control baseline management module, a six-dimensional risk control adaptation layer, a unified output layer, and a dynamic continuous adaptation and monitoring module. The global risk control baseline management module serves as the system's base layer, incorporating a standardized security baseline library and a six-dimensional standard template library. This library covers standard data across six dimensions: hardware parameters, sensor data, network environment, system identifiers, container operating environment, and root / mask risks, providing a unified reference benchmark for all subsequent adaptation operations. The six-dimensional risk control adaptation layer, located below the global baseline layer, consists of six parallel risk control adaptation modules, each corresponding to one of the six dimensions. Each module independently performs feature simulation, cleaning, and smoothing operations for its corresponding dimension, including standardized generation of hardware parameters and cleaning of abnormal fields; unified simulation and noise modeling of full-dimensional sensor data; simulation of the unified network baseline and cleaning of cloud data center IP features; standardized rewriting of system identifiers and removal of virtualization fields; deep hiding and purification of container virtualization features; and unified detection, identification, and repair of high-risk root / mask risks. Data processed by each adaptation module is uniformly merged into the normalized risk control environment output layer at the bottom. This layer outputs a consistent, clean, and realistic physical machine-level risk control environment across six dimensions, ensuring consistent risk control performance across devices. In addition, a dynamic continuous adaptation and monitoring module is located on the right side of the system. This module monitors environmental changes in each risk control dimension in real time and automatically triggers a full-process re-adaptation when a change is detected, maintaining the continuous stability and consistency of the risk control environment. Figure 1 The system structure shown is a specific implementation of the six-dimensional full-stack closed-loop risk control adaptation architecture of the present invention, which clarifies the hierarchical relationship and data flow between modules.

[0031] This invention pioneers a six-dimensional integrated full-stack risk control heterogeneous smoothing architecture, breaking through the limitations of traditional single-dimensional spoofing. It is the first to achieve a unified risk control adaptation system that fully covers six dimensions: hardware, sensors, network, system identification, container environment, and root / mask detection. It is a rare all-dimensional cloud phone risk control unification technology in the industry, and its technical integrity far exceeds that of existing single-point solutions.

[0032] (ii) S200: Perform a full-dimensional environmental scan on all types of heterogeneous cloud phones connected to the network, collect native feature data and risk exposure points of each device in each risk control dimension, compare the collected native feature data with the standardized risk control template library dimension by dimension, and identify the differentiated features and risk items of each device.

[0033] Specifically, when a self-built cloud phone, Baidu cloud phone, or Huawei cloud phone is connected to the system, the first step is to perform device access and environment detection. A full-dimensional environment scan is conducted on the various heterogeneous cloud phones connected, collecting native feature data and risk exposure points of each device in each risk control dimension. Then, a six-dimensional risk control feature difference analysis is performed, comparing the collected native feature data with a standardized risk control template library dimension by dimension to identify the differentiated features and risk items of each device.

[0034] Through the above-mentioned comprehensive environmental detection and difference analysis, we can identify various fragmentation issues of risk control characteristics caused by differences in virtualization architecture, container solutions, and system customization strategies of cloud phones from different manufacturers.

[0035] (III) S300: Based on the identification results of differentiated features and risk items, unified simulation and feature smoothing processing are performed on the hardware parameter dimension, sensor data dimension, network environment dimension, and system identification dimension, respectively, and deep purification processing is performed on the container operating environment dimension to eliminate the original differences and virtualization residual features of each device in each risk control dimension.

[0036] Specifically, based on the identification results of differentiated features and risk items, hardware / sensor / network / system normalization and smoothing, as well as container virtualization feature deep cleanup, are performed respectively.

[0037] In one embodiment, the unified simulation and feature smoothing process of hardware parameters in S300 includes: Based on the sub-templates corresponding to the hardware parameter dimension in the standardized risk control template library, a standardized hardware parameter template library is established. The hardware parameters of heterogeneous cloud phones from multiple manufacturers are standardized and dynamically replaced according to the template library. The hardware parameters include IMEI, MEID, device serial number, manufacturer model, battery parameters, screen parameters, storage parameters, and CPU / GPU parameters. Furthermore, abnormal hardware fields, virtualization identifiers, and cloud device-specific parameters native to each manufacturer's devices are cleaned and replaced in batches to achieve normalized and realistic output of hardware risk control features for all devices.

[0038] Through the above processing, the hardware parameter fragmentation problem caused by differences in virtualization architecture and underlying drivers of cloud phones from different manufacturers is eliminated, so that all heterogeneous cloud phones can be normalized in terms of hardware risk control characteristics.

[0039] In one embodiment, the unified simulation and feature smoothing processing of sensor data dimensions in S300 includes: Standard data from sensor dimensions in a standardized risk control template library is used as the simulation benchmark to build a full-dimensional sensor simulation engine. This engine uniformly simulates real-time sensor data from gravity sensors, gyroscopes, magnetometers, distance sensors, light sensors, and attitude sensors, and adds natural dynamic noise and fluctuation data to prevent fixed parameter features from being identified by risk control. It also unifies the sampling frequency, data accuracy, and variation patterns of sensors from multiple devices, smoothing out the differences in risk control features between sensors from heterogeneous devices and achieving consistent adaptation of risk control features across sensors from different devices.

[0040] In one embodiment, the unified simulation and feature smoothing process of the network environment dimension in S300 includes: The system utilizes a standardized risk control template library to define standard environmental baselines for the network dimension, covering network risk control dimensions such as IP address, network type, DNS link, gateway parameters, network latency, routing characteristics, TLS fingerprints, and HTTP / HTTPS request characteristics. Addressing the network architecture differences among cloud phone vendors, it unifies the network environment baseline, cleans cloud data center IP characteristics, identifies devices on the same network segment, and identifies virtualized networks, simulating real mobile or broadband network environments to ensure consistent network risk control behavior across multiple devices and eliminate cluster-related risks.

[0041] In one embodiment, the unified simulation and feature smoothing process of the system identifier dimension in S300 includes: By calling upon the standard features of the system identifier dimension in the standardized risk control template library, a comprehensive system identifier management system is constructed, which uniformly adapts to Android version, system compilation fingerprint, system attributes, device build information, permission list, system services, process name and package management features; batch clears the cloud device identifier, virtualization system field and vendor-customized features that come with cloud phones from various manufacturers, and uniformly outputs standardized physical machine system environment features to avoid system-level virtualization risk control identification.

[0042] In one embodiment, the deep cleanup process in S300 at the container runtime environment level includes: Based on the clean environment standard of the container runtime environment dimension in the standardized risk control template library, the system detects and hides the container mount nodes, virtualization processes, cloud service resident processes, and container-specific environment variables of cloud phones; it also masks cgroup, namespace, and docker feature identifiers, uniformly simulates a clean physical machine runtime environment, eliminates differences in container environments from multiple vendors, and achieves complete unification of container environment risk control features.

[0043] Because commercial cloud phones generally retain container mounting characteristics, virtualization processes, and cloud device-specific identifiers, they cannot simulate the clean operating environment of a real physical machine and are easily identified as virtual devices by risk control systems. Therefore, deep purification of the container operating environment is one of the core aspects of this invention. Through the above processing, the cloud phone completely eliminates the residual characteristics of containers, virtualization, and cloud services, presenting a clean operating environment of a real physical machine.

[0044] This invention addresses the issue of heterogeneous cloud phones from multiple vendors by establishing a unified and standardized risk control output baseline. It completely eliminates virtualization remnants, vendor customization differences, and uneven capabilities associated with different cloud phone architectures, achieving completely consistent risk control performance across devices. Furthermore, addressing the industry challenge of completely concealing container characteristics in commercial cloud phones, it employs deep environment detection, process cleanup, mounting concealment, and variable purification technologies to thoroughly eliminate cloud device container identifiers, simulating a clean operating environment of a real physical machine with extremely high realism. Through dynamic noise algorithms and real-time data simulation, it overcomes the shortcomings of traditional fixed-parameter masquerading methods that are easily detected by risk control systems. Simultaneously, it unifies the network fingerprints and link characteristics of multiple devices, eliminating the risk of device cluster association.

[0045] (iv) S400: Performs a full-dimensional high-risk environment scan on each device after feature smoothing, accurately identifies risk items such as root privilege enabled status, mask modules, hidden plugins and injected processes, automatically performs environment repair and risk feature removal on the identified risk items, and performs a secondary environment verification on the repair results to confirm that the risk has been eliminated.

[0046] In one embodiment, the unified detection and closed-loop repair of high-risk Root / mask operating environments in S400 includes: Using the security baseline in the standardized risk control template library as the basis for risk assessment, a unified risk scan is performed on heterogeneous cloud phones from multiple manufacturers. The detection targets of the risk scan include root permission enabled status, Magisk module, hidden plugins, SU permission binary files, injected processes, tampered system files, and high-risk hidden environments. Based on the risk scan results, the system automatically performs environment repair, permission revocation, file restoration, process cleanup, and risk feature removal operations on the detected risk items.

[0047] Specifically, after feature smoothing, a high-risk environment scan for Root / mask is performed, such as... Figure 3 As shown, the system first performs a low-level environment scan, including root privilege / SU file detection, mask module / hidden plugin detection, and system tampering / abnormal process identification. Then, it determines whether there are any high-risk factors. If there are, risk signature removal and environment restoration are performed; if there are no high-risk factors, permission reset and security baseline solidification are performed, ultimately outputting a clean and secure device environment.

[0048] Figure 3The key feature of this invention is its innovative logic for differentiated high-risk environment detection and repair, which can accurately identify explicit and implicit root and mask risks. Through a closed-loop repair mechanism, it unifies the safety environment baseline of all heterogeneous devices, ensuring the consistency of risk control for cluster devices.

[0049] In one embodiment, performing a secondary environmental verification on the repair result in S400 to confirm risk elimination includes: After repair, perform a full-dimensional high-risk environment scan on each device and compare the scan results with the safety baseline in the standardized risk control template library. If the comparison is consistent, the risk is confirmed to be eliminated and the process proceeds to the normalized risk control environment output step. If the comparison is inconsistent, repeat the environment repair and risk feature removal operation until the comparison is consistent with the safety baseline.

[0050] This invention differs from traditional simple root detection. This solution can deeply identify hidden modules, fake root, injected processes, and hidden tampering risks, and achieve automatic feature smoothing and environment repair, forming a complete security closed loop of detection-identification-repair-unification, unifying the security baseline of all devices, and eliminating risk control interception caused by differences in device environment.

[0051] (v) S500: After processing through the above steps, the equipment will output a normalized and highly realistic equipment risk control environment according to the unified standard of the standardized risk control template library. During the operation of the equipment, the changes in the environment of each risk control dimension will be monitored in real time, and the risk control features will be dynamically refreshed to maintain the continuous stability and uniformity of the risk control environment.

[0052] After undergoing root / mask high-risk environment scanning and detection, risk closed-loop repair, and feature rewriting, a unified risk control baseline is output and continuous monitoring is performed.

[0053] Specifically, each device that has passed the secondary environment verification will output a normalized device risk control environment according to the unified standards of the standardized risk control template library. At this time, all connected self-built cloud phones, Baidu cloud phones, and Huawei cloud phones will exhibit consistent characteristics that are completely matched with the standardized risk control template library in six dimensions: hardware parameters, sensor data, network environment, system identification, container operating environment, and root / mask risk. The cross-device risk control performance will be completely consistent.

[0054] In one embodiment, the S500 monitors changes in the risk control environment across various dimensions in real time and dynamically updates risk control features to maintain the continuous stability and consistency of the risk control environment, including: During operation, the system monitors environmental changes across all risk control dimensions in real time, dynamically updates risk control features based on the monitored environmental changes, and re-triggers the execution of S200's full-dimensional environmental scan, dimension-by-dimensional comparison and differential feature identification, S300's multi-dimensional unified simulation and feature smoothing processing, and S400's full-dimensional high-risk environmental scan, risk identification and environmental remediation when environmental changes are detected, in order to maintain the continuous stability and uniformity of the risk control environment.

[0055] Through the aforementioned real-time monitoring and dynamic refresh mechanism, the risk control environment of the equipment remains consistent with the standardized risk control template library during long-term operation, thus maintaining the continuous stability and uniformity of the risk control environment.

[0056] This invention features a unique full-stack closed-loop risk control adaptation architecture of "six-dimensional risk control modeling - heterogeneous feature smoothing - high-risk environment detection - unified baseline output", which breaks through the limitations of traditional single-dimensional spoofing and is a rare all-dimensional heterogeneous cloud mobile phone risk control unification technology solution in the industry.

[0057] Compared with existing traditional risk control adaptation technologies, this invention has significant technological breakthroughs and practical value: 1. Most comprehensive coverage, filling industry technology gaps: It achieves six-dimensional full-stack risk control adaptation, covering hardware, sensing, network, system, container, and security risk scenarios that existing technologies cannot cover. It belongs to a highly scarce and innovative direction in the field of cloud phone risk control. 2. Completely resolve the issue of differentiated risk control for heterogeneous devices: Cloud phones from multiple vendors have a unified security baseline, unified environmental characteristics, and unified risk standards, resulting in completely consistent risk control performance across devices; 3. Significantly improve device realism and anti-identification capabilities: Deeply remove residual features of virtualization, containers, and cloud devices, dynamically simulate physical machine environments, and avoid the risks of risk control interception and batch blocking by various platforms; 4. Automated risk closed loop with extremely low operation and maintenance costs: It realizes automatic risk detection, automatic repair and automatic smoothing without manual intervention, and is suitable for large-scale cloud mobile phone cluster batch management scenarios. 5. Excellent compatibility and scalability: It is compatible with self-built, Baidu, and Huawei heterogeneous systems, and can be quickly expanded to other manufacturers' cloud phones. It has a high degree of universality and strong practicality.

[0058] In one embodiment, a unified simulation and detection adaptation system for cloud mobile phone full-dimensional risk control environment is provided, including: The standardized risk control template library construction module is used to build a standardized risk control template library for heterogeneous cloud phones from multiple vendors as a benchmark for unified output of the full-dimensional risk control environment. The standardized risk control template library includes six dimensions: hardware parameters, sensor data, network environment, system identification, container operating environment, and root / mask risk. The full-dimensional environment scanning and differential analysis module is used to perform full-dimensional environment scanning on various heterogeneous cloud phones connected to the network, collect native feature data and risk exposure points of each device in each risk control dimension, and compare the collected native feature data with the standardized risk control template library dimension by dimension to identify the differential features and risk items of each device. The multi-dimensional heterogeneous feature unified simulation and smoothing module is used to perform unified simulation and feature smoothing processing on the hardware parameter dimension, sensor data dimension, network environment dimension, and system identification dimension according to the identification results of differentiated features and risk items, and to perform deep purification processing on the container operating environment dimension to eliminate the original differences and virtualization residual features of each device in each risk control dimension. The high-risk operating environment detection and closed-loop repair module is used to perform a full-dimensional high-risk environment scan on each device after feature smoothing. It accurately identifies risk items such as root permission enabled status, masked modules, hidden plugins and injected processes. It automatically performs environment repair and risk feature removal on the identified risk items, and performs a secondary environment verification on the repair results to confirm that the risk has been eliminated. The normalized risk control environment output and dynamic continuous adaptation module is used to output a normalized and highly realistic equipment risk control environment for each device after the above steps according to the unified standard of the standardized risk control template library. During the operation of the equipment, it monitors the changes of each risk control dimension environment in real time and dynamically refreshes the risk control features to maintain the continuous stability and uniformity of the risk control environment.

[0059] Specific limitations regarding the unified simulation and detection adaptation system for a cloud phone's all-dimensional risk control environment can be found in the limitations of the unified simulation and detection adaptation method for a cloud phone's all-dimensional risk control environment described above, and will not be repeated here. Each module in the aforementioned unified simulation and detection adaptation system for a cloud phone's all-dimensional risk control environment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0060] The foregoing has provided a detailed description of the unified simulation and detection adaptation method and system for all-dimensional risk control environment of cloud mobile phones provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention, and the descriptions of the embodiments above are only for the purpose of helping to understand the core ideas of this invention. It should be noted that those skilled in the art can make several improvements and modifications to this invention without departing from the principles of this invention, and these improvements and modifications also fall within the protection scope of the claims of this invention.

Claims

1. A unified simulation and detection adaptation method for a cloud phone's all-dimensional risk control environment, characterized in that, Includes the following steps: S100: A standardized risk control template library is built for heterogeneous cloud phones from multiple vendors as a benchmark for unified output of the full-dimensional risk control environment. The standardized risk control template library includes six dimensions: hardware parameters, sensor data, network environment, system identification, container operating environment, and root / mask risk. S200: Performs a full-dimensional environmental scan on all types of heterogeneous cloud phones connected to the network, collects native feature data and risk exposure points of each device in each risk control dimension, compares the collected native feature data with the standardized risk control template library dimension by dimension, and identifies the differentiated features and risk items of each device. S300: Based on the identification results of differentiated features and risk items, unified simulation and feature smoothing are performed on the hardware parameter dimension, sensor data dimension, network environment dimension, and system identification dimension, respectively, and deep purification is performed on the container operating environment dimension to eliminate the original differences and virtualization residual features of each device in each risk control dimension. S400: Performs a full-dimensional high-risk environment scan on each device after feature smoothing, accurately identifies root privilege enabled status, masked modules, hidden plugins and injected process risk items, automatically performs environment repair and risk feature removal on the identified risk items, and performs a secondary environment verification on the repair results to confirm that the risk has been eliminated. S500: After processing through the above steps, each device outputs a normalized, highly realistic device risk control environment according to the unified standard of the standardized risk control template library. During the operation of the device, it monitors the changes in the environment of each risk control dimension in real time and dynamically refreshes the risk control features to maintain the continuous stability and uniformity of the risk control environment.

2. The method according to claim 1, characterized in that, The unified simulation and feature smoothing of hardware parameters in S300 includes: Based on the sub-templates corresponding to the hardware parameter dimension in the standardized risk control template library, a standardized hardware parameter template library is established. The hardware parameters of heterogeneous cloud phones from multiple manufacturers are standardized and dynamically replaced according to the template library. The hardware parameters include IMEI, MEID, device serial number, manufacturer model, battery parameters, screen parameters, storage parameters, and CPU / GPU parameters. Furthermore, abnormal hardware fields, virtualization identifiers, and cloud device-specific parameters native to each manufacturer's devices are cleaned and replaced in batches to achieve normalized and realistic output of hardware risk control features for all devices.

3. The method according to claim 1, characterized in that, The unified simulation and feature smoothing of sensor data dimensions in S300 includes: Standard data from the sensor dimensions in the standardized risk control template library is used as the simulation benchmark to build a full-dimensional sensor simulation engine. This engine uniformly simulates real-time sensor data from gravity sensors, gyroscopes, magnetometers, distance sensors, light sensors, and attitude sensors. Natural dynamic noise and fluctuation data are added, and the sampling frequency, data accuracy, and variation patterns of sensors from multiple devices are unified. This smooths out the differences in risk control characteristics between sensors from heterogeneous devices and achieves consistent adaptation of risk control characteristics across sensors from different devices.

4. The method according to claim 1, characterized in that, The unified simulation and feature smoothing process for the network environment dimension in S300 includes: The system utilizes standardized network-level baselines from a standardized risk control template library, covering network risk control dimensions such as IP address, network type, DNS link, gateway parameters, network latency, routing characteristics, TLS fingerprints, and HTTP / HTTPS request characteristics. Addressing the network architecture differences of cloud phones from various vendors, the system unifies the network environment baseline, cleans cloud data center IP characteristics, identifies devices on the same network segment, and identifies virtualized networks, simulating real mobile or broadband network environments to ensure consistent network risk control behavior across multiple devices.

5. The method according to claim 1, characterized in that, The unified simulation and feature smoothing process for the system identifier dimension in S300 includes: By calling upon the standard features of the system identifier dimension in the standardized risk control template library, a comprehensive system identifier management system is constructed, which uniformly adapts to Android version, system compilation fingerprint, system attributes, device build information, permission list, system services, process name and package management features; batch clears the cloud device identifier, virtualization system field and vendor-customized features that come with cloud phones from various manufacturers, and uniformly outputs standardized physical machine system environment features to avoid system-level virtualization risk control identification.

6. The method according to claim 1, characterized in that, The deep cleansing process for the container runtime environment in S300 includes: Based on the clean environment standard of the container runtime environment dimension in the standardized risk control template library, the system detects and hides the container mount nodes, virtualization processes, cloud service resident processes, and container-specific environment variables of cloud phones; it also masks cgroup, namespace, and docker feature identifiers, uniformly simulates a clean physical machine runtime environment, eliminates differences in container environments from multiple vendors, and achieves complete unification of container environment risk control features.

7. The method according to claim 1, characterized in that, Unified detection and closed-loop repair of high-risk root / mask operating environments in S400 includes: Using the security baseline in the standardized risk control template library as the basis for risk assessment, a unified risk scan is performed on heterogeneous cloud phones from multiple manufacturers. The detection targets of the risk scan include root permission enabled status, Magisk module, hidden plugins, SU permission binary files, injected processes, tampered system files, and high-risk hidden environments. Based on the risk scan results, the system automatically performs environment repair, permission revocation, file restoration, process cleanup, and risk feature removal operations on the detected risk items.

8. The method according to claim 7, characterized in that, S400 performs a secondary environmental verification of the remediation results to confirm that the risk has been eliminated, including: After repair, perform a full-dimensional high-risk environment scan on each device and compare the scan results with the safety baseline in the standardized risk control template library. If the comparison is consistent, the risk is confirmed to be eliminated and the process proceeds to the normalized risk control environment output step. If the comparison is inconsistent, repeat the environment repair and risk feature removal operation until the comparison is consistent with the safety baseline.

9. The method according to claim 1, characterized in that, S500 monitors changes in various risk control dimensions in real time and dynamically updates risk control features to maintain the continuous stability and consistency of the risk control environment, including: During operation, the system monitors environmental changes across all risk control dimensions in real time, dynamically updates risk control features based on the monitored environmental changes, and re-triggers the execution of S200's full-dimensional environmental scan, dimension-by-dimensional comparison and differential feature identification, S300's multi-dimensional unified simulation and feature smoothing processing, and S400's full-dimensional high-risk environmental scan, risk identification and environmental remediation when environmental changes are detected, in order to maintain the continuous stability and uniformity of the risk control environment.

10. A unified simulation and detection adaptation system for cloud mobile phone full-dimensional risk control environment, characterized in that, include: The standardized risk control template library construction module is used to build a standardized risk control template library for heterogeneous cloud phones from multiple vendors as a benchmark for unified output of the full-dimensional risk control environment. The standardized risk control template library includes six dimensions: hardware parameters, sensor data, network environment, system identification, container operating environment, and root / mask risk. The full-dimensional environment scanning and differential analysis module is used to perform full-dimensional environment scanning on various heterogeneous cloud phones connected to the network, collect native feature data and risk exposure points of each device in each risk control dimension, and compare the collected native feature data with the standardized risk control template library dimension by dimension to identify the differential features and risk items of each device. The multi-dimensional heterogeneous feature unified simulation and smoothing module is used to perform unified simulation and feature smoothing processing on the hardware parameter dimension, sensor data dimension, network environment dimension, and system identification dimension according to the identification results of differentiated features and risk items, and to perform deep purification processing on the container operating environment dimension to eliminate the original differences and virtualization residual features of each device in each risk control dimension. The high-risk operating environment detection and closed-loop repair module is used to perform a full-dimensional high-risk environment scan on each device after feature smoothing. It accurately identifies risk items such as root permission enabled status, masked modules, hidden plugins and injected processes. It automatically performs environment repair and risk feature removal on the identified risk items, and performs a secondary environment verification on the repair results to confirm that the risk has been eliminated. The normalized risk control environment output and dynamic continuous adaptation module is used to output a normalized and highly realistic equipment risk control environment for each device after the above steps according to the unified standard of the standardized risk control template library. During the operation of the equipment, it monitors the changes of each risk control dimension environment in real time and dynamically refreshes the risk control features to maintain the continuous stability and uniformity of the risk control environment.

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