3130 results about "Byte" patented technology

The invention provides a multilevel caching method and system based on CAN bus communication, and is applied to the field of data transmission. According to the method, the influence coefficient of the CAN bus is obtained by presetting load scenes (idle, low load and high load), whether the influence coefficient is limited by hardware is judged, the optimization direction is ensured to be accurate, data cache management is performed by adopting an annular buffer area, and the data transmission interval is dynamically adjusted and the data packaging strategy is optimized (small data merging, data alignment and filling byte reduction), so that the data transmission efficiency is improved. The CAN ID priority is dynamically adjusted by collecting communication data category information, it is ensured that high-priority data (such as ECU control signals) are transmitted in time, bandwidth occupation of low-priority data is reduced, in addition, the processing response speed of data frames is monitored, and if the processing response speed does not reach the standard, the data sending frequency is optimized, and the real-time performance is improved.

A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment. Upon receiving a user request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live stream of image data of a field of view of at least one camera, wherein the live stream includes imagery of at least a portion of the financial instrument. The method continues by extracting in real-time, based on the formation of byte array objects from the live stream of image data, data fields from a ranked sequence of imagery to be processed by an optical character recognition (OCR) program resident on the client device. The OCR process extracts one or more data fields from of the financial instrument that are communicated to a remote deposit server to complete the electronic deposit.

To realize efficient control processing.SOLUTION: In a game machine, a CPU is capable of executing: third processing which performs first processing of writing, by the CPU, 55H into a 1-byte first general-purpose register and then writing, by the CPU, a first general-purpose register value into a 1-byte first RAM, and performs second processing of writing, by the CPU, AAH into the 1-byte first general-purpose register and then writing, by the CPU, the first general-purpose register value into a 1-byte second RAM; and sixth processing which performs, after the third processing, fourth processing of writing, by the CPU, a first RAM value into the first general-purpose register and then comparing, by the CPU, the first general-purpose register value with 55H to determine whether or not they correspond to each other, and performs fifth processing of writing, by the CPU, a second RAM value to the first general-purpose register and then comparing, by the CPU, the first general-purpose register value with AAH to determine whether or not they correspond to each other.SELECTED DRAWING: Figure 130

Systems and methods for implementing sticky tokens are illustrated. One embodiment includes a machine-readable medium containing bytecode stored within an immutable ledger. The bytecode encodes a review token, comprising a token identifier. The review token comprises a content element, wherein: the content element comprises a text document; and the text document corresponds to a written review. The review token comprises a recipient identifier, wherein the recipient identifier identifies a blockchain address of an intended recipient. The review token comprises access control information, comprising a right to transfer the review token. Execution of the bytecode occurs automatically when the recipient identifier matches a current owner of the review token. Execution of the bytecode renders of the content element. Execution of the bytecode blocks the blockchain address of the intended recipient from possessing the right to transfer.

The invention discloses an encrypted malicious Trojan flow detection method based on a mask auto-encoder and multistage flow modeling, which comprises the following steps of: converting original bytes of encrypted malicious Trojan flow into a multistage flow modeling matrix, and extracting the arrival time interval of a data packet and the length sequence characteristics of the data packet; the matrix is segmented into small blocks which are not overlapped, each small block is mapped into a vector, and the vector is added with position coding information to serve as the input of an encoder; respectively capturing dependency relationships in the data packets and between the data packets by using a packet-level attention mechanism and a flow-level attention mechanism; fusing traffic behavior modes contained in a time sequence convolutional network explicitly modeling data packet time interval and a data packet length sequence; a self-supervision pre-training strategy based on a mask auto-encoder is adopted, a large amount of available label-free data is used for training the encoder, and a small amount of data is used for fine adjustment in downstream tasks, so that efficient encrypted malicious Trojan flow detection is realized. According to the method, different encrypted malicious Trojan traffic can be accurately detected by fully utilizing hierarchical structure characteristics of network traffic without depending on traffic statistical characteristics and a large amount of encrypted malicious Trojan traffic label data.

The invention discloses a network protocol reverse analysis method based on deep learning and a graph neural network, and the method comprises the steps: basic field detection: carrying out the byte-level feature extraction of a binary data stream through sliding window embedding, bidirectional LSTM coding and knowledge enhanced CRF decoding, and outputting a structured field labeling sequence; performing protocol format clustering based on the sequence: calculating a multi-dimensional similarity through an improved Needleman-Wunsch algorithm, realizing automatic classification of unknown protocols in combination with a dynamic density clustering algorithm optimized by LSH, and outputting a protocol cluster; and performing composite structure analysis based on the protocol cluster: constructing a protocol syntax tree based on a graph neural network, performing multiple rounds of message passing through a graph attention network GAT attention mechanism, identifying a nested structure and performing recursive analysis, and generating a multi-level protocol syntax tree. According to the method, the automation degree and accuracy of complex protocol analysis can be remarkably improved.

The invention relates to the technical field of computer application, and discloses a file read-write event monitoring method under an eBPF, which comprises the following steps: initializing an eBPF program, generating a kernel mode byte code and a user mode byte code, loading the kernel mode byte code into a kernel, and carrying out instrumentation in a file operation function to monitor file operation; initializing a preset buffer area for communication between a kernel mode and a user mode, and storing a file identifier of a to-be-monitored file in an eBPF mapping table; and when a file operation function is called, reading kernel structure body information, obtaining an identifier of an operated file, matching the identifier with the identifier in the mapping table, if the matching fails, discarding an event, and if the matching succeeds, judging that the file is in a monitoring range. Therefore, the monitoring of the file read-write event under the eBPF is realized.

The invention relates to an implementation method and device for an EtherCAT master station system based on a non-operating system, and the method comprises the steps: achieving the adaptation of an SOEM protocol stack to a hardware abstraction layer of an EtherCAT master station through the byte order processing based on a bit mask and a staged shift strategy and the EtherCAT frame receiving and transmitting control based on annular buffer area index management; on the basis of non-real-time task priority scheduling processing of a hardware timer, main circulation driven by a single-thread event and overtime monitoring of a blocking communication synchronization mechanism in combination with the timer, adaptation of an SOEM protocol stack to an EtherCAT master station operating system abstraction layer is achieved; through network scanning and communication parameter configuration, a memory management mechanism based on CoE standard mapping and a periodic position mode of a slave station, adaptation of an EtherCAT master station application layer is realized. According to the invention, through optimization and adaptation of the SOEM protocol stack, operation-system-free and microsecond-level communication jitter of the EtherCAT master station system is realized.

Examples described herein relate to a switch, when operational, that is configured to receive in a packet an indicator of number of remaining bytes in a flow and to selectively send a congestion message based on a fullness level of a buffer and indication of remainder of the flow. In some examples, the indicator is received in an Internet Protocol version 4 consistent Options header field or Internet Protocol version 6 consistent Flow label field. In some examples, the congestion message comprises one or more of: an Explicit Congestion Control Notification (ECN), priority-based flow control (PFC), and / or in-band telemetry (INT). In some examples, to selectively send a congestion message to a transmitter based on a fullness level of a buffer that stored the packet and the number of remaining bytes in flow, the switch is to determine whether the buffer is large enough to store the remaining bytes in the flow.

The invention discloses an interpretable fine-grained industrial control network intrusion detection method, which comprises a model structure part and a data processing part, and is characterized in that the model structure part comprises a byte-level interpretable module, a packet-level feature extraction module, a flow-level time sequence analysis module and a result output module; the data processing part comprises a byte-level network model, a packet-level network model, a flow-level network model and a data processing process, and the data processing process comprises data preprocessing, feature extraction and final intrusion detection decision making. Compared with the prior art, the deep learning-based intrusion detection system model has the advantages that the deep learning-based intrusion detection system model is provided and is used for detecting advanced attacks in an ICS network. Through byte, data packet and flow level feature extraction and fusion, the accuracy, interpretability and efficiency of intrusion detection are improved, and the method has significant advantages especially in a complex attack scene.

The invention provides a multi-target tracking method based on a YOLOv8 model and a Byte Track algorithm, and relates to the technical field of computer vision and edge equipment. The method specifically comprises the following steps: acquiring data of a plurality of images, performing format conversion, and constructing an image data set; constructing a DC-YOLOv8 network structure, and performing training by using the image data set to obtain a target detection model based on the DC-YOLOv8 network structure; obtaining a to-be-detected video stream, extracting continuous image frames from the to-be-detected video stream, and preprocessing the extracted image frames; and inputting the preprocessed image frames into the target detection model for target detection, and performing target tracking on all detected targets by adopting a Byte Track algorithm to obtain tracking trajectories of the tracked targets in all the image frames and generate a video stream. According to the invention, target tracking can be carried out on a video with many targets more smoothly.

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention discloses an Internet of Things protocol analysis method and device based on multi-modal AI and a medium, and relates to the field of industrial Internet of Things, and the method comprises the steps: collecting a protocol byte stream of industrial equipment and associated multi-modal physical data; the protocol byte stream and the multi-modal data are analyzed through the multi-modal AI, and multi-modal features are extracted and fused; inputting the data into a pre-trained protocol grammar generation model, outputting a protocol grammar tree of a communication protocol corresponding to the industrial equipment, and performing semantic decoding on the protocol grammar tree to obtain structured protocol data; obtaining a multi-modal physical measured value of the industrial equipment, verifying the structured protocol data based on the multi-modal physical measured value, and calculating a comprehensive confidence coefficient; and calibrating the structured protocol data based on the comprehensive confidence coefficient. Various industrial protocol messages are automatically identified and analyzed through the multi-mode AI, protocol parameters do not need to be manually configured, personal errors are reduced, and the data acquisition speed and efficiency are improved.

The invention relates to the field of byte code unshelling storage, in particular to a byte code storage method and device of an Android unshelling machine and computer equipment. The method comprises the following steps: monitoring all application programs in a system in real time, and carrying out pseudo-call dynamic class loading to generate byte code structure feature information; performing service module identification on the byte code structure feature information, and performing module security sensitivity quantification so as to obtain a security sensitivity quantized value of each service module; based on the security sensitive quantized value of each service module, performing byte code symmetric encryption and confusion encryption on the multiple application program service modules so as to obtain a normalized module encryption code and a confusion encryption compression code; and carrying out code-by-code independent packaging processing on the normalized module encryption code and the confusion encryption compression code, and carrying out distributed storage so as to construct a distributed encryption unit storage structure. According to the invention, complete and efficient shelling preservation is realized.

The invention discloses a memory allocation method and related equipment, which are applied to electronic equipment, and the method comprises the following steps: receiving a first memory request of an application program, the first memory request comprising a target memory space value; determining a virtual address matched with the target memory space value from a virtual memory space, and determining a target virtual address to be mapped from the virtual address; a to-be-mapped target physical address is determined from a physical memory space, whether the target virtual address and the size and byte boundary of the target physical address meet a first condition is judged, and the first condition comprises the alignment type of the byte boundary; if the first condition is met, determining an attribute parameter matched with the first condition; and creating a page table matched with the attribute parameter, and establishing a mapping relationship between the target virtual address and the target physical address. By adopting the embodiment of the invention, the memory access efficiency can be improved.

The invention provides a cold start optimization system and a cold start optimization method for a Python Serverless function. The system comprises a dependency analysis module, a module pre-compiling system, a hierarchical module loader, an inert import mechanism, a code cache optimizer and a self-adaptive optimization system. The dependency analysis module analyzes code dependency in a function deployment stage; the module pre-compiling system pre-compiles the source code into an optimized byte code; the hierarchical module loader loads modules of different hierarchies according to priorities; actual loading of an inert import mechanism delay module; the code cache optimizer caches a hotspot function and a module state; and the self-adaptive optimization system dynamically adjusts preloading and caching strategies. The system further comprises a JIT compiling intensifier, a state storage and quick recovery module, a memory management optimizer and a security mechanism. The method has the beneficial effects that the function cold start time is remarkably shortened, the function execution efficiency is improved, the resource utilization is optimized, and meanwhile, the safety and the expandability are ensured.

The invention provides an intrusion prevention method, a management unit, a system and a storage medium, and relates to the field of network security, and the method comprises the steps: collecting context operation data corresponding to a to-be-tracked asset object in a kernel, and carrying out the security management of the context operation data, and obtaining a first target security control strategy corresponding to the to-be-tracked asset object; and mounting a first target BPF byte code corresponding to the first target security control strategy in a Linux security module so as to monitor and defend the target asset object through the Linux security module. Or in response to the security policy configuration request, extracting a second target security control policy from the security policy configuration request; and when the second target security control strategy is activated, mounting a second target BPF byte code corresponding to the second target security control strategy in the Linux security module. Therefore, through the above intrusion prevention method, the embodiment of the invention can immediately block attacks and improve the convenience of maintenance and deployment.

The invention relates to a ZNS SSD-based B + tree index construction method for dynamic placement of cold and hot data. A persistent memory PM, a dynamic random access memory DRAM and a ZNS SSD hard disk are included. The persistent memory PM and the dynamic random access memory DRAM are directly connected with a CPU memory bus and have the capability of being accessed by the CPU according to bytes; internal nodes of the B + tree are stored in a dynamic random access memory (DRAM), and the internal nodes only store index information used for positioning leaf nodes; the pseudo leaf nodes of the B + tree are stored in a persistent memory PM, and only the key index information of the leaf nodes is stored in the pseudo leaf nodes; leaf nodes really storing the key value pairs in the B + tree are divided into hot leaf nodes and cold leaf nodes according to the data access frequency, the hot leaf nodes are stored in a persistent memory PM, and the cold leaf nodes are stored in a ZNS SSD. According to the method, the leaf nodes are quickly positioned by using the DRAM, so that the query time is shortened; hot leaf nodes are stored in the PM, and the real-time service read-write requirement is met; and hot data is stored in a high-speed DRAM and PM, so that the random read-write delay is reduced, and the performance is excellent in scenes such as big data analysis and the like.

The invention relates to the technical field of compiling, in particular to a byte code file optimization method and device, a byte code file execution method and device and an intelligent card. The method comprises the following steps: acquiring a byte code file to be optimized; bytecode instructions in the bytecode file are traversed in sequence, target bytecode instructions are obtained based on instruction operation codes of the target bytecode instructions, and target operand information carried in the target bytecode instructions is obtained according to instruction types of the target bytecode instructions, determining an operand storage area corresponding to the instruction type; determining an index of each piece of target operand information in the corresponding operand storage area to obtain each corresponding target index value; and according to each target index value and a preset index value length, repackaging the corresponding target bytecode instruction to obtain an optimized bytecode file. Therefore, the problem that an executable bytecode file of an existing intelligent card application occupies a large space can be effectively solved.

The invention relates to the technical field of network security and vulnerability detection, in particular to an intelligent contract vulnerability detection method and system based on hybrid fuzzy test optimization, and the method comprises the steps: carrying out the static analysis of compiled EVM byte codes, generating a first test case set, carrying out the fuzzy test, and introducing taint analysis to track the transmission of taints in an instruction stream; in the fuzzy testing process, according to the length change of the instruction counting set within the execution frequency threshold value, symbol execution processing is triggered when the length change value is 0, in the symbol execution processing process, a constraint cache reuse strategy is adopted to solve an obtained constraint expression, and a solving result is cached and reused, so that a fuzzy test result is obtained; a use case value reaching a complex branch can be quickly generated through symbolic execution and is fed back to the fuzzy engine to assist the fuzzy engine in exploring a new path to capture more deep vulnerabilities; and meanwhile, test predictions are designed for three intelligent contract vulnerabilities of authority control, transaction sequence dependence and denial of service, and vulnerability detection is more accurate.

The invention discloses a malicious encrypted traffic decryption method and system based on memory data analysis, and the method comprises the steps: reading process activity data of a target computer; determining a target malicious process, and obtaining a target malicious process identifier; reading the memory space of the target malicious process to obtain memory data; capturing a network communication data packet of the target malicious process; determining candidate memory data; sequentially moving and extracting byte sequences; calculating an information entropy value and a hash value of each byte sequence; selecting byte sequences of which the information entropy values are greater than a preset threshold value and the hash values are not repeated in all byte sequences, and generating a key candidate set; and performing decryption operation on the network communication data packet by using each byte sequence in the key candidate set as a decryption key to obtain an effective decryption key and decrypted data content. According to the invention, decryption and analysis of malicious encrypted traffic are realized.

The embodiment of the invention provides a byte code generation method and device and a machine code generation method and device, and the byte code generation method comprises the steps that a target source code is obtained, first processing is applied to the target source code, and the first processing comprises the steps that a target statement used for integer operation in the target source code is replaced with a preset target function; according to the first processed target source code, a target byte code is generated, the target byte code comprises a byte code instruction generated according to a preset function, and the byte code instruction indicates that integer operation corresponding to the target statement is executed through a preset machine instruction sequence and overflow check for the integer operation is executed.

The invention relates to the technical field of network space security, and provides a DoH malicious tunnel traffic detection method and system based on feature fusion, and the method comprises the steps: carrying out the preprocessing of obtained to-be-detected DoH traffic data, carrying out the sequence segmentation processing, obtaining a Token sequence, and extracting features based on a byte sequence feature extractor; statistical features are extracted and standardized, features are extracted through a statistical feature extraction sub-network, and statistical feature vectors are obtained; fusing the byte sequence feature vector with the statistical feature vector to obtain a classification result; the byte sequence feature extractor and the statistical feature extraction sub-network extract features, and training is carried out by adopting a semi-supervised learning framework of dynamic pseudo-tag screening for non-tag training samples. According to the method, multi-modal features are fused, a semi-supervised learning mechanism is introduced, malicious DoH traffic generated by multiple DNS tunnel tools is effectively identified under a limited annotation data set, and the detection precision and generalization ability are improved.

The invention discloses a distribution network line facility defect detection method based on line magnetic field variable characteristics, and relates to the technical field of magnetic variable measurement. According to the method, high-precision spatio-temporal data are obtained through multi-point synchronous non-contact magnetic field sensing, codes are efficiently preprocessed through self-adaptive voxel division and bytes, and the detection accuracy of the distribution network line facility defects is improved; and the line topology information is deeply fused to Token for representation. A pre-trained large language model is combined with low-rank adaptation (LoRA), sliding window attention and an external memory bank for efficient reasoning, and understanding of long sequences and topological association is enhanced. Characterization quality is improved through mixed mask self-supervised learning, and multi-task (classification, positioning and scoring) output is optimized by adopting uncertainty weighted loss. And privacy protection federal training is supported. And finally, an interpretable diagnosis report is generated, and an alarm and work order process is automatically performed. The accuracy, efficiency, intelligent level and automation degree of distribution network fault detection are remarkably improved, and safe and reliable operation of a power grid is guaranteed.

The invention relates to the field of risk data visual tracing, and provides a visual track tracing method and system for a risk checking task, and the method comprises the steps: analyzing a heterogeneous data stream, separating a source metadata set, calculating an original structure entropy fingerprint, intercepting an end feature, embedding a global tracking identifier, and generating a packaging data package; generating transformed service data by using dynamic byte code instrumentation, constructing a runtime execution context and a differential snapshot, and transmitting an instantiated track node object according to a shunt delivery strategy; mapping and instantiating the logical topology execution graph based on the Hash fragments, and marking a pollution state and generating a total element execution link graph when the logic topology execution graph is abnormal; the method comprises the steps of generating a topology summary data packet, rendering a real-time state view, responding to physical interaction operation to construct a retrieval request data packet, reconstructing a full-amount service load through topology backtracking and reverse data evolution, and generating an attribution view in combination with static codes and rule description. According to the method, a dynamic trajectory tracing and full-link risk visualization mechanism of massive heterogeneous data is constructed.

Embodiments of the present disclosure enable users to efficiently verify digital data produced by queried databases, even when that data is differentially-private (e.g., satisfying the conditions of differential privacy in order to protect sensitive or private data). In addition to the query result, a database computer can provide the client with a non-interactive zero-knowledge proof (NIZK), data that the client can use to verify the digital data contained in the query result, without revealing any private data to the client. Various innovations, including vectorized proofs, enable the database computer to generate proofs that require less data (e.g., when measured in bytes) than most NIZK proof systems. Consequently, these proofs can be transmitted and verified more quickly and efficiently. Embodiments of the present disclosure can make use of partially or homomorphic commitments and efficient vector proof techniques to achieve these performance improvements.

The invention discloses an anti-quantum-attack key generation method and device, equipment and a medium, which are used for improving the key generation efficiency and further improving the digital signature efficiency. The method provided by the invention comprises the following steps: determining a one-time signature type value; and respectively determining a first parameter and a second parameter used for generating a private key, generating a random number of a preset byte number, and taking the random number as a secret seed, further generating a plurality of first messages based on the one-time signature type value, a first parameter, a second parameter and a secret seed; grouping the plurality of first messages according to parameters used for representing the parallel processing capability of a preset hash function; for the first messages in each group, determining hash values corresponding to the first messages in the group in parallel by adopting the preset hash function; then, utilizing a hash value corresponding to each first message to obtain a first secret original image array; and generating a private key by using the first secret original image array.

The invention discloses a method, a device, equipment and a medium for realizing multi-service instance configuration, in the method, a Java compiler is called to scan a source code, a class marked with multi-instance container annotations is obtained, the class defines a plurality of service instances, and each service instance is marked with a corresponding multi-instance basic annotation; based on a preset annotation processor, traversing the multi-instance basic annotation corresponding to each service instance, obtaining a class attribute value and a prefix attribute value, and obtaining a first configuration class corresponding to the first automatic assembly class; according to the prefix attribute value, processing the byte code of the first configuration class and the byte code of the first automatic assembly class to obtain a second configuration class and a second automatic assembly class; and adding the second configuration class file and the second automatic assembly class file to the resource directory to realize configuration of the service instance. Thus, by introducing special multi-instance configuration annotations, namely, multi-instance container annotations and multi-instance basic annotations, the official automatic assembly class is expanded and customized, and multi-service instance configuration is achieved.

The invention relates to an encrypted traffic classification method and device, computer equipment and a medium, and the method comprises the steps: carrying out the preprocessing of original traffic, and obtaining a session image of a data packet header and a session image of a payload; extracting global features and spatial-temporal features of the header and the load according to the session image of the packet header of the data packet and the session image of the effective load; fusing the global features and the spatial-temporal features of the head and the load through adaptive weighting to obtain initially fused head features and load features; fusing the head feature and the load feature which are initially fused together through adaptive weighting to obtain a final feature vector; and classifying the original traffic according to the final feature vector. In the whole process, the situation that the same byte value has different meanings in the head and the effective load in practical application is fully considered, and accurate encrypted traffic classification can be realized.

The invention discloses a code obfuscation method, device and equipment and a storage medium, and belongs to the field of financial science and technology, and the method comprises the steps: obtaining an original byte code file corresponding to an application program source code; identifying to-be-confused data in the original byte code file, and confusing and renaming the to-be-confused data according to a mapping name of the to-be-confused data in the naming mapping table to obtain a first confused file corresponding to the original byte code file; identifying a control flow in the first confusion file, and performing confusion recombination on the identified control flow to obtain a second confusion file; and performing confusion processing on methods contained in various types in the second confusion file to obtain a target confusion file. According to the method, the maintainability and the resistance of the application program source code are enhanced, the risk that the application program source code is decompiled is reduced, the safety of the application program source code is improved, and then the safety of the application program is improved.