118 results about "Android application" patented technology

The invention discloses an android container rendering optimization method based on cross-domain hard real-time Fen synchronization, which comprises the following steps: by taking a swan-mong system as a host and an android system as a container, creating a hash table, a synchronous thread and a shared memory corresponding to a GPU core when the host is started, acquiring VSync cycle registration callback, transmitting shared memory FD to the container, and finishing shared memory mapping and alignment by the container. Registering a GPU queue to complete callback; when the Android application is started, a container obtains a queue and a physical address of a rendering buffer area, creates a Fen and binds the Fen to the queue, after the queue is submitted, metadata is written into a shared memory to inform a host, after the host receives the metadata, nodes are created and stored in a hash table, and an overtime timer is registered; after the GPU completes the command queue, the container calls back an update state and a verification value to notify the host, and after the host is verified to be valid, the corresponding hash table is updated, the timer is reset, and asynchronous screen loading is triggered; and the host executes buffer area synthesis and submission of the display equipment to finish on-screen, so that the stability of the rendering frame rate is improved, and the reliability of cross-domain synchronization is ensured.

The invention discloses a container application on-screen method based on texture full-process optimization, which comprises the following steps: taking a swan-gap system as a host and an Android system as a container, distributing a plurality of first buffers by the host, screening out a first texture format by the container, writing original textures of the format generated by rendering into frame buffers, identifying a dirty area of the rendered textures, and displaying the original textures in the frame buffers; packaging the texture data of the region, storing the packaged texture data into a shared memory, transmitting a file descriptor FD to a host, binding an idle first buffer setting priority, a state, a generation timestamp and a texture data pointer at the same time, and obtaining the texture data by the host through the FD; the host establishes a fixed thread task pool, executes multiple tasks in parallel to obtain on-screen texture data, and then updates the state of the first buffer and a texture data pointer; and finally, the host queries the first buffer with the processed state, reads the texture data after sorting according to the priorities and the generation timestamps to finish on-screen, and resets the state of the first buffer to be idle, so that the on-screen delay is effectively reduced, and the actual frame rate of the Android application is improved.

The invention provides an Android application task automation method based on a user interaction description diagram, and belongs to the technical field of automation. Comprising the steps that a first stage comprises user interaction description graph generation and aims at obtaining a user interaction description graph of an application; and outputting the user interaction description diagram of the application by taking the Android project as input. And the second stage comprises task execution path planning and adaptive interactive operation generation. According to the method, static program analysis is combined with the semantic reasoning ability of the LLM, the structured user interaction description graph is automatically constructed from the source code of the target application, the user interface elements, the interaction logic and the state jump relation are covered, and the problems of operation blindness and the like caused by lack of application prior knowledge before task execution of the LLM are solved. An initial operation instruction sequence can be generated in combination with user task requirements; through an adaptive instruction re-planning mechanism, state fault tolerance and path optimization in a task execution process are realized, and the task completion rate in a complex scene is remarkably improved.

The invention discloses a multi-window display optimization method for android applications of a swan-Monte platform, which comprises the following steps: after an android container is started, creating a first shared memory containing a plurality of partitions on the swan side, distributing and binding a component ID when an android application component is started, configuring GLES resources, creating a double-buffering queue and a GLES context, and displaying the GLES resources in the double-buffering queue. A shared image resource handle is generated, a mapping table of a component ID, a window ID and a texture identifier is established, context and the component ID are verified during component rendering, a difference region is calculated and processed to generate first data and a checksum after pixel data are rendered, a data head and the first data are written into a shared memory partition, and a gap side is notified; after the gap side verification is passed, information is extracted to generate a texture identifier, a Surface buffer area is associated, and finally Android and gap window data are synthesized and submitted to display hardware to be displayed on a screen.

The invention provides a cabin integration method of whole vehicle self-adaption platform service based on SOA and a vehicle, and relates to the technical field of vehicle SOA service, the method comprises the following steps: deeply integrating functional components of a whole vehicle self-adaption platform into an Android operating system, and embedding a communication module into an Android hardware abstraction layer; the original biochemical deployment of the SOA service in the cabin domain is realized; meanwhile, an efficient and low-delay service calling interface is constructed by expanding a communication mechanism between Android native processes, so that the Android application can directly call the whole vehicle level SOA service, and the dependence of traditional cross-domain communication on a vehicle-mounted Ethernet and a central gateway is avoided; besides, processor core resources are dynamically allocated in combination with the function security level of the SOA service, so that the real-time performance and execution reliability of high-security-level tasks are guaranteed, and the overall resource utilization efficiency of the system is improved.

The invention discloses an Android malicious software dynamic detection method fusing API sequence semantics and graph structure features. The method comprises the following steps that an Android application program runs in a simulator environment, a runtime log is monitored and collected, an API method name sequence is extracted from the log, and calling sequence semantic information is reserved; constructing a global API dependency relationship graph based on a point-by-point mutual information PMI theory; generating joint feature representation by combining semantic embedding and graph structure embedding, including semantic embedding, graph structure embedding and joint embedding; inputting the fusion embedding into a pre-training BERT encoder, extracting features through a self-attention mechanism, and outputting a detection result through a classifier by using [CLS] flag bit vectors; according to the method, the detection performance is remarkably improved, and the method has high practical application value.

The invention discloses an Android cross-process stain detection method based on transaction graph reconstruction, and relates to the technical field of mobile terminal information security, and the method comprises the following steps: a deployment monitoring module intercepts Binder communication between Android applications; adding a stain mark for the sensitive data source, and attaching the mark during inter-process transmission; recording information of communication transactions between each process, constructing a transaction graph, and restoring paths for transmitting sensitive data between different processes in the graph; performing traversal analysis on the transaction graph, and searching for a path of spreading the data carrying the taint marks from the source application to the target application; and if the target application does not authorize to acquire the sensitive data or send the sensitive data out of the equipment, judging that the sensitive data is leaked and outputting an alarm. According to the method, flowing of privacy data can be tracked across application boundaries, automatic detection of cross-process leakage of user sensitive information caused by application collusion is achieved, and the accuracy and comprehensiveness of privacy leakage detection of the Android platform are remarkably improved.

The invention discloses an APK package volume optimization method and device, a storage medium and electronic equipment, and relates to the technical field of Android application development.The method comprises the steps that unpacking processing is conducted on different versions of APKs of a target application, and package volume parameters corresponding to the different versions of APKs are obtained; detecting the packet volume parameters among different versions of APK packets by adopting detection rules corresponding to different detection indexes to obtain to-be-optimized packet volume parameters corresponding to different detection indexes; performing optimization priority ranking on each packet volume parameter to be optimized to obtain a packet volume parameter queue to be optimized; and optimizing each to-be-optimized packet volume parameter one by one by adopting a parameter optimization mode corresponding to each to-be-optimized packet volume parameter in the to-be-optimized packet volume parameter queue to obtain an APK packet volume optimization result. According to the method, the APK package volume optimization efficiency and accuracy can be improved.

The invention provides an Android APK reinforcement method and system oriented to high-strength code protection, and belongs to the technical field of computer software, and the method comprises the steps: obtaining a resource call chain during the operation of an Android application through a reflection heat loading technology, and generating a dynamic encryption key stream; performing segmented encryption on the key resource according to the dynamic encryption key stream to obtain encrypted resource data; analyzing a bottom-layer instruction sequence of the encrypted resource data by adopting an instruction-level semantic analysis technology, and generating a semantic conversion template; performing dynamic re-signature on the package body according to the verification result to obtain a re-signed application package body; monitoring the re-signed application package body through a runtime reverse protection mechanism, and obtaining an abnormal behavior log; if the dynamic injection attack is detected in the abnormal behavior log, protection logic encryption is activated, and an irreversible protection module is generated; and updating an encryption strategy of the data stream during operation according to the irreversible protection module to obtain an optimized resource protection scheme. According to the method and the device, the resource security during Android application running is remarkably improved.

The application discloses a kind of API sequence semantics and graph structure feature fusion Android malware dynamic detection method, including the following steps: running Android application in simulator environment, monitoring and collecting runtime log, extracting API method name sequence from log, retaining call order semantic information;Based on point mutual information PMI theory, global API dependency graph is constructed;Combined with semantic embedding and graph structure embedding, joint feature representation is generated, including semantic embedding, graph structure embedding and joint embedding;The fusion embedding is input into pre-training BERT encoder, and the feature is extracted through self-attention mechanism, and the detection result is output by the classifier with [CLS] flag bit vector;The application significantly improves the detection performance, and has strong practical application value.

The invention relates to an external display screen driving method and system based on an Android system, an external display screen is connected with Android equipment through a low-speed serial interface, and the method comprises the steps that S1, a Native layer initialization function is called through a JNI interface on an Android application layer; s2, an LVGL graphic library is loaded on a Native layer; s3, registering and adapting external display screen hardware of the low-speed serial interface through a display driving interface of the LVGL graphics library; s4, executing a color space conversion algorithm on the Native layer, and converting color graphic data output by the LVGL graphic library into color format data suitable for the target external display screen; and S5, transmitting the converted display data to an external display screen through a device driving interface of the Linux kernel. According to the method, the lightweight LVGL graphics library is integrated into the Android application through the JNI technology, and the display screen connected through the low-speed serial interface is directly driven in the user space, so that the efficient and flexible display driving is realized under the condition that the source code of the Android system is not modified, and the multi-screen different display function is supported.

Embodiments of the present specification provide a method, device and storage medium for detecting invocation of a trusted execution environment (TEE) by an Android application. The method comprises: obtaining a data packet of a software to be detected; obtaining an identifiable file from the data packet based on a reverse engineering algorithm; identifying whether the software to be detected invokes a TEE function based on a preset TEE function vector set and the identifiable file, the TEE function vector set containing vectors representing general functions / interfaces, indirect invocation functions / interfaces or specific TEE functions / interfaces; obtaining a function vector of the invoked TEE function, text data and logical structure data of a module in the software to be detected that invokes the TEE function from the identifiable file when the software to be detected invokes the TEE function; and determining a functional module to which the invoked TEE function belongs based on the function vector of the invoked TEE function, the text data and the logical structure data. The technical solution provided by the present application solves the problem of insufficient precision in detecting TEE in the prior art.

The application discloses an Android application logic bomb semantic detection method and device based on LLM, and belongs to the technical field of intelligent terminal security; the device comprises an APK reverse analysis graph extraction module, a sensitive API knowledge base construction and suspicious CFG generation module, a cross-function semantic correlation and structured representation module, an LLM semantic reverse reasoning identification and explainability analysis module; through converting a target Android APK into an LLM understandable semantic expression form, LLM understandable APK program semantic information is generated; a logic bomb sensitive API knowledge base is constructed and a suspicious CFG is generated; cross-function call context semantic information of the suspicious CFG is constructed and is subjected to serialization and structured processing; a reverse reasoning identification logic bomb based on factual evidence semantic features and an evidence report are generated; the application can convert an APK file into an LLM easily understandable semantic expression form, and then effectively identify a logic bomb hidden in an intelligent terminal application, and has good explainability.

The invention relates to the technical field of computer software, in particular to a method for automatically generating an Android system Binder communication SDK (Software Development Kit), which comprises the following steps: defining an AIDL interface source file and compiling to generate a JAR packet; defining an interface configuration JSON file; executing a pre-generation program, and creating an annotation mark Java file; integrating the annotation processor and compiling to generate a final SDK file, wherein the generation process of the final SDK file comprises the following steps: registering and triggering the annotation processor, analyzing the annotation, analyzing an AIDL interface method, constructing a code model and generating the SDK file; and using the generated SDK file. The technical problems that in existing Android application development, due to the fact that Binder communication bottom layer template codes are manually written, the development efficiency is low, the codes are redundant and prone to errors, and unified maintenance is difficult are solved, and normalization and consistency of communication codes among multiple services are fundamentally guaranteed.

The invention discloses a method and a system for monitoring use time of Android application equipment, belongs to the technical field of Android application development, and aims to solve the technical problem of how to monitor foreground applications and count use time length data of each application through barrier-free services in Android services. Comprising the following steps: creating a system-level barrier-free service, registering a window monitoring event for monitoring window state change in a configuration file of the barrier-free service, and maintaining two member variables; when a window state change is detected based on the barrier-free service, generating a data record, and updating a member variable; when the window switching event is triggered, filtering the new window through a pre-configured multi-layer filtering mechanism; monitoring screen-off and screen-on broadcasts of the Android device, and generating a data record; and locally storing data records generated in the window switching event processing and the screen-off and screen-on broadcast monitoring process, and uploading the locally stored data records to the cloud.

The present disclosure provides a method and device for restoring obfuscation of an Android string, a computer device and a medium, comprising: converting intermediate bytecode of an Android application installation package data into a first representation file and a second representation file; performing semantic understanding on the second representation file to obtain a control flow graph corresponding to a target method body; identifying obfuscation points based on the control flow graph corresponding to the target method body to obtain a plurality of target candidate sentences; generating a corresponding slice standard representation for each target candidate sentence; reversely constructing a program dependency chain based on the plurality of target candidate sentences to obtain a data flow graph structure; determining a minimum code subset based on the data flow graph structure and the slice standard representation corresponding to each target candidate sentence; performing a reorganization operation on the minimum code subset to generate a subprogram; embedding the subprogram into a class file to obtain a target class file; and executing the target class file through the first representation file to obtain a decrypted string. Thus, the success rate of string decryption is improved.

The utility model provides an audio playing device in a vehicle and the vehicle, and the device comprises a state button which is disposed in the vehicle; the camera is arranged in the vehicle and is used for shooting an in-vehicle image in the vehicle; the first connecting end of the control unit is connected with the signal transmission end of the state button, the second connecting end of the control unit is connected with the signal transmission end of the camera, and the third connecting end of the control unit is connected with the loudspeaker control end of a bus communication unit of the vehicle. And a fourth connecting end of the control unit is connected with an Android application program interface of the vehicle.

The invention relates to the technical field of computers, and discloses a native function calling method and system.The native function calling method and system are applied to a first bridging module, the first bridging module is a bridging module corresponding to a front-end application, and when the front-end application has a calling requirement for calling a native function of an operating system, calling parameters corresponding to the calling requirement are obtained; based on the calling parameter, generating a calling message in a preset format according with data processing of the operating system; a bridging message sending method is called, the calling message is sent to a second bridging module, so that the operating system executes the native function corresponding to the calling requirement based on the calling message, and the second bridging module is a bridging module corresponding to the operating system. Through cooperation of the first bridging module and the second bridging module, it is not needed to rewrite a large number of original plug-in codes or redesign communication logic, and rapid adaptation of the Android application and the plug-in to the swan-gap system can be achieved.

The invention relates to the technical field of computer security, in particular to a dynamically updated Android malicious software continuous learning detection method, which comprises the following steps of: acquiring and sequencing historical Android application samples according to time to construct a training set, and training a hierarchical comparison classifier; new applications are collected regularly to form a to-be-tested batch, and the current classifier is used for prediction; for each sample in the to-be-detected batch, executing pseudo loss uncertainty calculation to obtain an uncertainty score of the sample; according to the score, selecting a predetermined number of most uncertain samples for labeling, and obtaining a real label; adding the new labeled sample into the training set, and performing incremental training by adopting a hot start mode based on the weight of the current classifier to obtain an updated classifier; and circularly executing, and detecting the new application in the next period by using the updated classifier. According to the method, the labeling cost can be remarkably reduced, the concept drift can be effectively coped, and efficient, stable and continuous malicious software detection is realized.

The application discloses a multi-window display optimization method for an Android application of a Hongmeng platform, and the method comprises the following steps: after an Android container is started, a first shared memory containing multiple partitions is created on the Hongmeng side; when an Android application component is started, the component ID is allocated and bound, the GLES resource is configured, the double-buffer queue and the GLES context are created, the shared image resource handle is generated, and the mapping table of the component ID, the window ID and the texture identification is established; when the component is rendered, the context and the component ID are checked; after the pixel data is rendered, the differential area is calculated and the first data and the checksum are generated and processed; the data header and the first data are written into the shared memory partition, and the Hongmeng side is informed; after the Hongmeng side is verified, the information is extracted to generate the texture identification and the associated Surface buffer; finally, the Android and Hongmeng window data are synthesized and submitted to the display hardware to complete the on-screen display.

The invention discloses a global monitoring and collecting method and device for Android application crash information and a medium, belongs to the technical field of mobile terminal technologies, and is used for solving the problems that existing Android terminal crash information monitoring is not comprehensive, specific reasons of crash are difficult to locate, use experience of a user is not facilitated, and user experience is poor. And the processing complexity of a developer is increased. The method comprises the following steps: performing data monitoring processing on crash information of a Java layer by customizing a default uncaptured abnormal processor of a system, and collecting and obtaining Java layer crash structured data; the method comprises the following steps: intercepting key crash signals of a Native layer through a global signal processor, and asynchronously acquiring and obtaining Native layer crash related data; performing structured assembly of related data packets on the Java layer crash structured data and the Native layer crash related data, and performing asynchronous transmission to obtain a crash report; and transmitting the crash report to a server through the asynchronous queue to obtain crash report information.

This invention discloses a malware detection method based on adaptive node-edge joint masking. First, a function call graph of an Android application is constructed, and the structural and semantic features of nodes and edges are extracted. Then, a masking strategy is adaptively determined based on the relative importance of nodes and edges in the graph structure, and joint masking is applied to nodes and edges. On this basis, a graph neural network model is trained, enabling the model to learn stable and discriminative representations even when some graph structures or features are damaged. Finally, the trained model is used to detect malware in Android applications. This invention demonstrates effectiveness in improving the robustness and engineering practicality of Android malware detection models.

The invention belongs to the technical field of information security, and particularly relates to an Android malicious software detection method based on enhanced operation code semanteme, which comprises the following steps: performing static analysis on an Android application sample to obtain a function call graph and an operation code sequence; performing sensitive API screening on the function call graph to obtain a simplified API call graph; assigning an operation code in the operation code sequence of each node in the simplified API call graph according to the relation between the weight of the node to which the operation code belongs and the weight of the whole API call graph; inputting the node vector representation into a Bi-LSTM model to obtain a node time sequence vector representation; clustering based on the node time sequence vector representation to obtain enhanced feature representation; taking the simplified API calling graph represented by the embedded enhanced features as input, taking a malicious software detection result as output, and training a graph convolutional neural network; inputting the enhanced feature representation of the to-be-detected Android application and the simplified API call graph into the detection model to obtain a detection result; according to the method, the problem that the detection model performs deep semantic extraction on the graph is solved, and the detection accuracy is improved.

Systems and methods are disclosed to develop and use a tool to enable an application package to be instrumented with automatic real user monitoring (RUM) without accessing the original source code. A package, such as Android application bundle (AAB) or Android package kit (APK), is imported and decoded and a generated source code file and / or manifest is obtained. Instrumentation is then added at a location corresponding to a code signature in the generated source code file (e.g., an operation to be instrumented before and / or after the operation). The generated source code file is then compiled and packaged into an APK and / or AAB file. The resulting application package is available for downloading, installation, and use on user devices with the instrumentation having been automatically added and without access to the original source code.

The application provides an Android malware detection method based on multi-view learning, and belongs to the technical field of Android malware detection. The method comprises the following steps: 1, decompiling the Android application to be detected, extracting the function call relationship, opcode sequence and permission information, constructing the function call graph and extracting the sensitive behavior subgraph; 2, using the pre-trained code representation model CodeBERT to perform semantic coding on the method-level opcode sequence, and fusing the permission features to generate the node semantic vector; 3, constructing a multi-view graph neural network, generating multi-view input through a feature mask enhancement strategy, introducing a multi-hop adaptive attention mechanism to realize dynamic aggregation of multi-hop neighborhood information, and simultaneously adopting a multi-channel coding structure for learning complementary graph representation; 4, fusing the multi-channel node representation, constructing a relationship matrix through key-value mapping, and generating a graph-level representation through convolution and pooling, and finally inputting a classification model to realize Android malware detection.

The application discloses a kind of based on symbol execution's malicious code obfuscation technology static repair method, belong to software analysis technical field, main in solving the malicious software static escape problem when existing static analysis technique faces obfuscated malicious software.The main scheme includes the basic static analysis to the tested Android application to obtain the sensitive API list and original graph feature in the tested Android application;According to the sensitive API list obtained, write hook function to simulate sensitive API list function, generate function replacement table, use symbol execution technology to simulate running Android application, replace part of function according to function replacement table in this process, finally obtain the overall execution state information and the block information of basic block when Android application simulation executes;According to state information and block information, prune and reconstruct original graph feature to realize static repair, exclude static obfuscation information, and mine effective graph feature therein.

The application discloses a POS machine terminal application hierarchical management and control method and system, and relates to a method for managing and controlling the hierarchical management and application installation of each entity in a POS machine terminal management system, in particular to a virtual hierarchical management and Android application installation control method of a POS machine terminal management system; the method comprises the following steps: generating a first certificate; generating a second certificate based on the first certificate; issuing a third certificate based on the first certificate; performing terminal initialization based on the second certificate and the third certificate; generating a fourth certificate based on the first certificate; signing an application package; verifying the signature and installing the application package; the hierarchical management and control comprises a user level, an administrator level and a super administrator level; after an agent orders a batch of terminals from a manufacturer, a user agent can directly distribute the terminals from the inventory without passing through the manufacturer; the technical scheme realizes the hierarchical management of the manufacturer, the agent and the user, improves the delivery speed of the agent, reduces the inventory risk, and reduces the terminal maintenance cost of the manufacturer and the agent.

The invention discloses a Telephony process optimization system of a vehicle-mounted TBox, and relates to the field of vehicle-mounted communication, and the system comprises an Android application layer which is used for calling a Telephony Manager interface of a framework layer through monitoring and obtaining operation so as to obtain telephone related information or monitor telephone state changes; the framework layer comprises a TelephonyManager interface and a TelephonyRegistry Manager interface, and the TelephonyRegistry Manager interface is used for receiving a monitoring and obtaining request from the Android application layer, and calling a TelephonyRegistry unit, and the framework layer comprises a TelephonyManager interface and a TelephonyRegistry Manager interface which are used for receiving the monitoring and obtaining request from the Android application layer; the framework-services framework service layer comprises a TelephonyRegistry unit which is used for receiving the request of the framework layer and calling the HAL unit, and a TelephonyRegistry unit which is used for receiving the request of the framework layer and calling the HAL unit; the HAL unit is used for calling the data information of the SOA unit and providing the called data information to the TelephonyRegistry unit; the SOA unit is used for calling the data information of the TBox unit and providing the called data information to the HAL unit; and the TBox unit is used for providing network state information and receiving calling of the SOA unit. According to the scheme, the newly added HAL process and SOA process are introduced, the mode of accessing the TBox is optimized, Android upgrading and TBox and other version upgrading can be compatible to the maximum extent, and the input manpower and maintenance cost is low.

The application discloses a kind of dynamic depth analysis engine construction methods based on genetic evolution algorithm, belong to software analysis technical field, main in solving the problem of low code coverage and malicious behavior trigger rate of existing dynamic analysis technology, main scheme includes step 1: the UI level capture of being tested Android application is carried out to obtain UI component information in the tested Android application, obtain UI level information.Step 2: specified UI event trigger is realized by injecting event function.Step 3: capture software dynamic behavior characteristic data and realize malicious behavior dynamic detection using neural network model.Step 4: event function sequence is generated and screened using genetic evolution algorithm, and the injected event function is changed by feedback regulation.The application finally realizes the dynamic optimization of UI event sequence by combining with feedback regulation while detecting malicious behavior, and realizes the identification of Android malicious software based on dynamic analysis with high trigger rate.

The present disclosure provides a method and device for starting an Android application in a Linux system, electronic equipment and storage medium, relating to the technical field of computer. The method comprises: listening to an icon on the desktop of the Linux system being triggered, the Linux system running an Android container, the Android container having started an Android system service, obtaining a desktop file corresponding to the icon, determining the trigger request to start the Android application according to the desktop file, obtaining the file package information of the Android application and sending it to the Android system service, and triggering the Android system service to start the Android application according to the file package information. The technical scheme of the present disclosure can start the Android application in the Linux system without the synchronous operation of the Linux and Android dual systems, reduces the complexity, improves the starting speed of the Android application, and improves the performance of the system.