74 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 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 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 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.

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 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 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.

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 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 an Android malicious program detection method based on a small sample learning model, and comprises the following steps: step 1, collecting initial sample data and performing preliminary preprocessing to obtain an original Dalvik operation code sequence of the initial sample data; step 2, performing Smali embedding representation on the original Dalvik operation code sequence of the initial sample data obtained in step 1; step 3, obtaining newly generated sample data; step 4, outputting a three-order fusion feature tensor with Android application malicious code semantic information; and step 5, providing a classification result of detecting whether an Android application is a malicious program. According to the method, the sample data enhancement is performed on the initial small sample data, the sample category imbalance problem is alleviated, and the problems of missing long sequence context association, high artificial feature engineering cost and high false detection rate are solved.

The invention belongs to the technical field of software automation testing, and provides an application state snapshot detection and bifurcation execution method and device, equipment and a medium, and the method comprises the steps: distributing instance identifiers and session identifiers for virtual instances of a running target Android application, and constructing a mapping table of the instance identifiers and the session identifiers; according to the mapping table, an incremental snapshot and fingerprint technology is adopted to collect snapshot content of the virtual instance according to the triggering opportunity, the snapshot content is stored in an associated mode according to the mapping table, and a snapshot generation process is determined through a session access index table; performing state recovery and operating environment isolation on the bifurcation instance according to the photo generation process; and creating a plurality of sub-process instances for each bifurcated instance, and debugging the bifurcated instances according to the debugging strategy and the modification configuration to obtain a debugging result. The method has the beneficial effects that the abnormal points can be carried and reproduced, and the detection accuracy and efficiency of faults, especially even faults, are improved.

The invention relates to an Android system SOA (service oriented architecture) communication middleware architecture based on vsomep, which is integrated in an Android system, runs as an independent process, and communicates with one or more Android application processes on the upper layer through a Binder IPC (internet protocol computer); the architecture comprises: a vsomeip protocol stack layer, which is used for realizing an SOME / IP communication protocol; the vsomeipBinding adaptation layer adopts a dynamic and static combination mode to decouple the protocol stack and the upper layer application; ara: a com core layer used for realizing a service API, managing a service instance and providing a security mechanism; and the HIDL interface layer is used for realizing Binder IPC communication with the Android application layer. A Manifest dynamic configuration mechanism and a LockDelay concurrency control mechanism are further introduced, and the problem of cross-process dynamic loading of service configuration files in the Android environment and the problem of conflicts during multi-process concurrency creation of service instances are solved respectively. According to the technical scheme, the stability, adaptability and maintainability of the SOA middleware in the Android system are remarkably improved.

The invention discloses an android application-oriented unauthorized private data transmission behavior detection method, which is used for analyzing the behavior of an android application by starting from application traffic and comprising the following four steps of: firstly, capturing original traffic in an application silence state; then traffic semantic keywords are extracted through traffic semantic analysis; identifying an entry function by using a traffic semantic keyword, extracting a key function call chain from the start of the entry function to the end of an encryption / coding function, and reducing functions in the call chain to form a key function set; and finally, performing instrumentation analysis on the key function, and inputting the traffic content and the runtime information of the key function into a large language model to judge the unauthorized privacy data transmission behavior. By means of the method, the privacy data which are transmitted through the plaintext traffic, the encrypted traffic and the like without user authorization of the application can be accurately recognized, the compliance of software is checked, and the privacy of the user and the data safety are guaranteed.

This invention discloses a barcode recognition method for pre-terminal junction boxes based on an Android application, belonging to the field of image recognition technology. It employs a YOLOv4 target detection network to detect 20 target types, including the pre-terminal junction box body, ports, and terminals, thereby significantly improving the accuracy of barcode area positioning. A relative position stability filter is used to match ports and barcode terminals, determining the correspondence between multiple ports and barcode terminals, ensuring that each inserted barcode terminal accurately finds its corresponding port number. Finally, a barcode recognition module is used to identify the barcode information. This method facilitates more convenient, faster, and more accurate fiber optic information entry and management for relevant personnel using highly flexible and portable handheld devices such as mobile phones, solving the problems of low efficiency, incorrect barcode information entry, and high operational difficulty in current manual fiber optic line maintenance.

This invention discloses a multi-view few-sample Android malware classification method based on optimal guidance matching, belonging to the field of information security technology. The invention includes constructing multi-view grayscale images, training a backbone network, optimal guidance matching classification, and dynamic fusion of multiple views. First, the method extracts permissions, APIs, components, and intent features of the Android application to construct multi-view grayscale images. Then, it trains a backbone network that integrates attention mechanisms and self-supervised rotation prediction to extract discriminative features with geometric structure awareness. Valid guidance samples are identified through optimal guidance matching, and category similarity scores are calculated. Finally, adaptive weights are generated based on view confidence, and the multi-view scores are dynamically fused to complete the classification. This invention improves the accuracy and robustness of malware family classification in few-sample scenarios, reduces noise interference, and is suitable for rapid and accurate identification of malware.

A curiosity-driven Android app automatic testing method includes the following steps:1) a left part represents a pre-processing assembly which achieves abstraction of an Android app state based on an Android interface structure; 2) a right part is curiosity-driven reinforcement learning module which maintains a historical access state set and optimizes an exploration policy constantly to guide a test to find more new states under the guidance of a reward function based on a curiosity thought; and 3) a middle part represents an advanced guidance module of a deterministic finite automaton (DFA), where the DFA is constructed during operation to record all access states and frequencies thereof; and if the new states are not explored within a given time budget, an AndroidExplore selects the most curious state as a starting point of a next exploration according to global information of the DFA.

The invention discloses an application management method and system for external storage equipment, and belongs to the technical field of application management. According to the method, application installation files in an external storage device are processed in a classified mode, recognizable files are directly installed, and an Android compatible storage library is constructed for unrecognizable files; counting the number of files, marking application affiliation, and generating a recognition feature cluster; the recognizable degree of each resource request object is evaluated, and the installation request is correspondingly processed; and constructing an installation state matrix, comparing the historical installation smoothness with the current installation smoothness, and optimizing the Android compatible storage library. The system correspondingly comprises four modules and a subdivision unit. The problems that in the prior art, an OpenHarmony system depends on a network to install the Android application, the adaptation efficiency is low, and operation is tedious in the non-network environment are solved, efficient application management in the off-line environment is achieved, the installation adaptation success rate and user operation convenience are improved, and the method is suitable for non-network or intranet scenes such as special industries and confidential units.

The invention relates to an Android malicious software detection method based on a multi-evidence fusion mixed heterogeneous graph. The method comprises the following steps: collecting an Android application data set and preprocessing the Android application data set; extracting a function call graph and a runtime event graph of the application program through static analysis and dynamic analysis respectively; utilizing a multi-evidence similarity fusion mechanism, combining code semantics, permission consistency and time frequency characteristics, associating static function nodes with dynamic event nodes, and constructing a hybrid heterogeneous graph; performing feature coding on nodes and edges in the graph; a deep learning framework is built, an improved reliability calibration heterogeneous graph model is adopted, and the model comprises confidence calibration attention and a reliability gating mechanism; and a final classifier is obtained through model iteration training. According to the method, the static structure and the dynamic behavior characteristics can be effectively fused, the noise is suppressed through the reliability of the automatic calibration edge, and the accuracy and robustness of Android malicious software detection are remarkably improved.

The invention provides an Android application integrity verification method based on double-anchor trigger chained trust extension, which comprises the following steps: respectively implanting anchor points in an installation process and an operation process, and acquiring a verification request of a to-be-verified application program under the condition that the installation anchor points or the operation anchor points are triggered; analyzing the verification request to obtain quintuple fingerprint information; and performing matching verification on the quintuple fingerprint information and the layered credible description set to obtain an integrity verification result. According to the method provided by the invention, anchor points are implanted in the installation process and the operation process respectively, under the condition that the installation anchor points or the operation anchor points are triggered, the verification request of the to-be-verified application program is obtained, the verification request is analyzed, the quintuple fingerprint information is obtained, the quintuple fingerprint information and the layered credible description set are subjected to matching verification, and the verification efficiency is improved. According to the method and the device, the running or updated third-party application installation package is comprehensively and effectively verified, and the safety of all application software in the installation and running process is improved.

The application discloses an Android static taint analysis device and method based on taint search association, the device is provided with a decompilation module, a word segmentation module and a semantic analysis module connected in sequence, the method adopts one or more parallel taint propagation edges pointing to taints in different callback methods, wherein, the taint search association only searches and analyzes taint-related codes, effectively searches and associates taint-related statements according to rules, to construct an accurate and complete taint propagation path graph, and simultaneously adopts taint alias acquisition, solves the alias analysis problem in taint analysis, and realizes Android static taint analysis containing accurate alias analysis. The method can improve the accuracy and analysis efficiency of taint analysis, and especially solves the problem of taint analysis accuracy decline caused by the characteristics of Android applications.

The application provides an audio processing method and device for an Android system, and the method comprises the following steps: registering a sound card device through a Linux ALSA sound card driving function; setting a first interface and a second interface of the sound card device, the first interface is used for receiving an audio stream, the second interface is used for sending the audio stream, and a piece of cache is applied to a kernel through a driving of the sound card device; forwarding when audio stream data written through the second interface is detected, and the written data is read by an Android application through the first interface. Compared with the prior art, the audio stream processed by an audio algorithm service is written into a sound card device through the second interface by registering the sound card device, the sound card device is simultaneously used as a sound input device of the Android application, the purpose of reading the audio stream of the audio algorithm service by the Android application is achieved, the data forwarding process is simplified, the processing difficulty of the audio stream output by the algorithm service is reduced, and the portability of the algorithm service is improved.

This invention provides a smart battery charging and discharging simulation method and related equipment, applied to cloud gaming services. It obtains the battery level, charger connection status, charging and discharging speed parameters, and upper and lower battery level thresholds at the start of the cloud gaming service through a standard Android application programming interface (API), and then activates a battery simulation switch. When the battery simulation switch is activated, charging and discharging are simulated on an information processor thread based on the battery level, upper and lower battery level thresholds, and charger connection status. The simulation results are obtained, and the battery level is updated according to the simulation results and charging and discharging speed parameters to obtain the updated battery level. Compared with existing technologies, this invention automatically discharges when the battery level reaches the upper limit and automatically charges when it reaches the lower limit, forming a continuous charging and discharging cycle, thus realizing automatic charging and discharging cycle control of the smart battery.