13 results about "Internet fraud" patented technology

The invention discloses a multi-dimensional graph series-parallel and visualization method for telecommunication network fraud cases based on a graph database. According to the method, intelligent series-parallel analysis across time-space cases is realized by constructing a knowledge graph containing various entities such as cases, personnel, accounts and equipment and deeply fusing a hard association matching algorithm and a soft similarity graph algorithm. The system has the functions of white list filtering, time sequence behavior analysis, NLP information extraction and the like, and can effectively identify criminal gangs, track fund flow directions and mine crime modes. And finally, dynamic visual display is carried out through a plurality of interactive views such as a force-oriented graph, a time axis and a mulberry-based graph, and clue intelligent pushing and report automatic generation are supported. According to the method, the case association mining depth, the serial-parallel analysis efficiency and the interpretability of a complex network are remarkably improved, and an efficient technical support is provided for the public security organization to fight against telecommunication network fraud.

This application discloses a method and system for consolidating evidence of network fraud through multimodal semantic consistency verification, comprising: collecting and modally identifying multimodal electronic evidence and generating original data fingerprints; extracting semantic information from each modal of evidence across five dimensions: time, amount, identity, behavior description, and geographical location; constructing a cross-modal evidence semantic association graph with a heterogeneous graph structure including evidence nodes, element nodes, and relational edges, and establishing attribution edges and cross-modal association edges based on element categories using differentiated semantic similarity calculation methods; generating initial feature vectors for each node, inputting the semantic association graph into a graph attention network model for consistency inference, performing consistency, contradiction, and uncertainty classification on cross-modal association edges, and obtaining a global consistency score; and performing differentiated on-chain consolidation operations based on the score to generate consolidation certificates. This invention can automatically discover semantic contradictions between different modalities of evidence, improving the quality of electronic evidence consolidation.

This invention proposes a chat topic recognition method and system based on text tag prediction. The method includes the following steps: acquiring and preprocessing the chat data information to be processed; processing the preprocessed chat data information using a trained deep learning model, including sentence encoding and sequence labeling; and identifying specific content from the chat data information using the deep learning model and further processing it. By introducing Sentence Bert to extract semantic features from chat statements, and using sentences as semantic units input into the model, the length limit of the feature labeling network is greatly expanded. The feature labeling method is used to label behaviors commonly seen in online fraud, ensuring that even if the wording is updated, the truly crucial inducement behaviors remain largely unchanged. By modifying the convolutional neural network model, the model can perform sequence labeling tasks while retaining the high inference performance of the convolutional neural network.

The invention relates to an information acquisition analysis and intelligent detection closed-loop linkage system and method for an internet fraud-related carrier, and belongs to the technical field of network security and fraud-related risk disposal. Aiming at the problems of disjunction of acquisition and detection, leak detection of homologous carriers, low response efficiency, no closed loop of results and the like in the prior art, a collaborative linkage architecture of an acquisition system, a detection system and a management and control platform is constructed; through black and white list query, standardized feature extraction, multi-model fraud-related detection, homologous clue supplementary collection, task priority management and result double-path closed-loop feedback, full-process closed-loop management and control of collection-detection-feedback-disposal is realized. According to the method, the accuracy, the coverage rate, the response efficiency and the overall prevention and control capability of fraud-related risk disposal are effectively improved.

Mobile phone software is used for network social contact, has various methods for preventing network fraud, and helps a user to judge network fraud through measures of logging in a historical list by the user, verifying a video through an address, forcing the video, evaluating various authenticity basis and the like. By adopting the methods, the network fraud can be effectively, conveniently and quickly prevented, and the labor cost of a software operator is saved.

Some embodiments implement a targeted notification of potential victims of online fraud, such as fishing, among others. A security server protects a plurality of client devices by using telemetry data harvested from each device to construct a device-specific client profile. The client profile indicates a typical manner in which the respective device is used (e.g., which applications are installed and how much each installed application is being used). Features extracted from incoming messages are used to identify fraudulent messages and divide an incoming message stream into clusters. In response to identifying a cluster including fraudulent messages, some embodiments identify the recipients of cluster messages, and select a notification target from among the protected client devices according to a similarity between a client profile of the notification target and client profiles of the message recipients. A security notification is sent to the notification target.

Provided are a risky website detection method and apparatus based on a large language model, the method comprising: inputting website information into a detection model to obtain a discrimination result, which is output by the detection model, so as to determine whether a website to be subjected to detection is an impersonation website, wherein the detection model is obtained by constructing a reward function by means of a group-wise reciprocal rank index, constructing unsupervised contrastive feedback on the basis of a set of similar websites, and training a large language model. The present application efficiently and accurately identifies and prevents impersonation cyber fraud activities by using strong semantic understanding and generation capabilities of the large language model and in view of a group-wise contrastive feedback reinforcement learning mechanism and fine-grained analysis of latent semantic enhancement.

The application discloses a fraud-preventing method applied to a remote control scene. The application takes a remote control platform as a role of device hardware information management, device network information management, user information management and user behavior information collection and management. In order to cope with the occurrence of network fraud cases in the remote control scene, the application provides fraud-preventing coping strategies in three stages of "prior risk prediction and avoidance", "in-process risk detection" and "post-process risk feedback", so that the remote control is fully prevented from being frauded.

The invention relates to the field of telecommunication network fraud prevention, in particular to a telecommunication network fraud scene analysis engine design method, system and device and a medium. The method comprises the steps that an original data set is acquired and screened; extracting original features corresponding to the screened original data set through a preset fraud scene model, and performing weighted scoring on the original features to obtain weighted features; converting the weighted features into a data set of an associated scene; and performing fraud scene analysis on the data set of the associated scene through a preset fraud scene model to obtain a scene analysis result, and safely outputting the scene analysis result through an open interface. According to the method, an original data set is processed by utilizing scene model instantiation configuration, an open associated scene data set is generated by utilizing different application scenes, and model arrangement and task scheduling are carried out by multiple model parties.

The invention discloses a telecommunication network fraud damage mode analysis method, which belongs to the technical field of mode analysis, and comprises the following steps of: through an LERT model, carrying out part-of-speech tagging, named entity recognition and dependency syntax analysis on sentences in a desensitization alarm text to obtain trigger words so as to form embedded vectors; through a MoE model, according to the embedded vector, obtaining weight coefficients corresponding to a plurality of experts, so as to screen the previous expert to execute processing, and combining to obtain a processing result, so as to obtain a candidate argument tag corresponding to each trigger word, and form an output tag sequence corresponding to the sentence; and according to the tag sequence, in combination with the embedded vector, obtaining a classification role tag through a softmax classification layer of the MoE model. Through the design of the multi-task collaborative architecture and the MoE model, the problems of feature expression singleness, behavior association fragmentation, event extraction and mode mining disjunction, insufficient dynamic adaptability and the like are effectively solved.

The application discloses an old-age anti-fraud prevention and control intelligent assistant platform based on artificial intelligence and belongs to the technical field of network security.The platform comprises a website security evaluation module, a user behavior analysis module, a matching judgment module and a behavior risk protection module; the website security evaluation module collects website text data, determines whether a website is related to fraud after extracting fraud risk features by means of a fraud risk category model; the user behavior analysis module obtains user mobile phone daily behavior data, divides group categories and establishes individual behavior baselines; the matching judgment module performs matching analysis on the operation behavior data of the old-age group on the related fraud websites and the individual behavior baselines, generates a behavior risk score, and triggers the behavior risk protection module when the behavior risk score exceeds 30 points; and the behavior risk protection module immediately forcibly terminates the related operation and sends an alarm.The application can effectively prevent and control network frauds suffered by the old-age people and improve the anti-fraud protection capability through the collaborative work of the multiple modules.

The present disclosure provides a method for detecting scam website middle pages and an electronic device, the method comprising: obtaining a webpage address and constructing a sample library based on the webpage address; extracting features of samples in the sample library to obtain target features; using the target features as training samples to iteratively train a convolutional neural network and obtain performance information of the neural network after each iteration; performing back propagation on the performance information of the neural network to update parameters of the convolutional neural network and save gradient values of the back propagation; wherein the performance information of the neural network is the accuracy of detecting scam website middle pages; in response to determining that the trained convolutional neural network meets a preset convergence condition, stopping updating the parameters of the convolutional neural network and outputting a model for detecting scam website middle pages. The present application can mark and detect that a user is diverted or jumped to a middle page of a scam website, thereby addressing the problem of network fraud under a complex network.

The invention discloses a network fraud early warning method on a mobile terminal, and belongs to the technical field of fraud early warning. The system mainly comprises a monitoring analysis layer, a multi-mode output engine and an early warning management module. The monitoring analysis layer comprises a monitoring module and an analysis module, the monitoring module is used for monitoring whether the behavior of the subject is triggered or not, and the analysis module is used for uploading subject information to a server to analyze whether the subject is involved in fraud or not; the multi-modal output engine comprises a visual early warning unit, a physical feedback unit and an authority management module; the visual early warning unit comprises a banner notification reminding module and a Toast reminding module, the physical feedback unit comprises a vibration reminding module and a voice broadcast reminding module, and the authority management module is used for enabling a user to customize and select switch banner notification reminding and voice vibration reminding. According to the network fraud early warning method on the mobile terminal, the effect of combining multiple early warning modes is achieved.