16 results about "Web traffic" patented technology

A system and method for automated bulk creation of Universal Resource Locators (URLs) with embedded Urchin Tracking Module (UTM) parameters is disclosed. The method processes input data, including endpoint URLs and UTM parameters, through pre-processing steps such as data cleansing and format standardization. The method appends UTM parameter-value pairs to endpoint URLs, optionally encoding the parameters. The generated tracking URLs are stored in a structured database for seamless integration with analytics tools, enabling reliable tracking and actionable insights into web traffic and user behavior.

A system and method for defending against AI-driven web traffic interference is provided. The system and method include: receiving, by an agent, a secret device ID and a web token provided by an authentication server; receiving an instruction to modify a request to a web service stored in a web server; embedding within a WebAssembly (WASM) component executed by the agent, a first portion of an encryption key and a second portion of the encryption key; transmitting the second portion of the encryption key to the authentication server; assembling, at runtime within the WASM component, an actual encryption key by combining the first portion and the second portion of the encryption key; encrypting, using the actual encryption key, the web token in the WASM component to generate a plurality of authentication tokens; and sending a request with the generated plurality of authentication tokens to the authentication server.

This invention discloses a method and apparatus for identifying dark web websites in scenarios with concurrent access across multiple tabs. The method includes: acquiring network traffic packets of a website being viewed and extracting directional sequence features from the network traffic packets; dividing the directional sequence features into multiple sub-sequence features based on a multi-sliding window, and inputting the multiple sub-sequence features into a neural network model to extract preset pattern features; analyzing the correlation of the preset pattern features using a target website identification model to obtain a probability calculation result of the target website being accessed; and obtaining the target website identification result from the website being identified based on the probability calculation result and a preset classification model. This invention can effectively extract key information of accessed dark web websites from obfuscated dark web traffic, achieve accurate website identification across multiple tabs, and exhibit good robustness in dynamic and defensive scenarios.

The application provides a dark web traffic classifier based on natural scene statistics and an anti-attack defense method, and belongs to the technical field of network security. The method comprises the following steps: obtaining dark web traffic original data to form an original data set; using four attack algorithms to attack each data in the original data set to obtain four kinds of adversarial attack samples; using a natural scene statistics method to characterize the dark web traffic original data and the adversarial attack samples generated by one of the four attack algorithms to obtain a parameter set of a generalized Gaussian distribution and an asymmetric generalized Gaussian distribution; training a detector using each parameter set; adding Gaussian noise to the data in the original data set, and training an autoencoder using the data after the Gaussian noise is added; combining the detector and the autoencoder into a two-layer defense mechanism; and identifying unknown traffic data by using the two-layer defense mechanism, feeding unknown traffic data with a classification result of attack traffic to the autoencoder for reconstruction to obtain benign data.

The application relates to the technical field of data analysis, and discloses a website traffic management method and system based on a load balancer, which comprises the following steps: acquiring real-time state information and real-time load information of a plurality of servers, inputting the real-time state information and the real-time load information into a preset load decision model to obtain real-time load decision data; configuring the load balancer according to the real-time load decision data to determine network traffic borne by each server; periodically acquiring performance index data of a website system under the real-time load decision data, and evaluating and optimizing the performance of the preset load decision model based on the performance index data of the website system. The preset load decision model is used to dynamically adjust the configuration of the load balancer, the load balancing accuracy and the resource utilization rate are improved, the decision and implementation results are periodically evaluated and optimized, the efficiency and robustness of the system are ensured, and the traffic load balancing demand of effectively managing a large website or application is met.

A computer-implemented method for digital fingerprint obfuscation is disclosed. The computer-implemented method includes training a machine learning model to classify web traffic data into one or more personas. The computer-implemented method further includes identifying, using the trained machine learning model, a particular persona of a user based, at least in part, on a user's real traffic data generated during a current user session. The computer-implemented method further includes generating synthetic traffic data based, at least in part, on the identified particular persona of the user.

Techniques for a deep learning approach for real-time scanning attack Uniform Resource Locaters (URLs) detection using Innocent Until Proven Guilty (IUPG) learning framework and custom web traffic volume features are disclosed. In some embodiments, a method for a deep learning approach for real-time scanning attack URLs detection using an IUPG learning framework and custom web traffic volume features includes pre-filtering network traffic associated with a URL for an inline scanning attack detection service, determining a plurality of features for the URL, applying the plurality of features for the URL to an IUPG-trained deep learning model for inline detection of scanning URLs, and performing an action in response to a determination that the URL is a scanning URL is disclosed herein.

The invention discloses a website statistical analysis method, device and equipment and a storage medium, one-button deployment is carried out based on a Docker containerization technology, firstly, a lightweight script in a website page is deployed, basic visitor behavior data not related to the personal identity of a user is collected, the volume of the lightweight script is smaller than 3 KB, and the lightweight script is loaded and executed in an asynchronous mode; encrypting and compressing the acquired basic visitor behavior data, sending the data to a server, storing the data in a database configured by a website host, and analyzing and calculating the data stored in the database to obtain a website traffic core index; and displaying the website traffic core indexes through a visual billboard. Quick installation and configuration can be realized by adopting one-button Docker deployment, extremely low influence on website performance is ensured by embedding an asynchronous script smaller than 3KB, and user privacy and data security can be thoroughly protected only by collecting non-personal identity data and encrypting, compressing and transmitting the non-personal identity data.

Systems and methods to manage and efficiently perform authorization of multiple proxy clients are disclosed. Furthermore, systems and methods to measure and check whether the web traffic of one or more client devices has reached a permissible limit of web traffic assigned by the proxy service provider. Specifically, a proxy is configured to gather and save authorization information of one or more clients within its memory. Therefore, the proxy server can verify and authorize one or more clients by utilizing the data from its memory. Furthermore, the proxy is configured to measure and report the utilized web traffic of one or more client devices to a messaging platform. In another aspect, systems and methods to check whether one or more client devices have reached a permissible amount of web traffic limit are disclosed.

This invention relates to the field of network security technology and discloses a method and system for identifying malicious traffic in intelligent Web application firewalls based on a CNN-LSTM hybrid neural network, aiming to address the technical shortcomings of traditional WAFs and existing detection models. This invention constructs an end-to-end intelligent Web traffic detection system, achieving malicious payload deobfuscation through adaptive recursive decoding. It employs a hybrid model combining multi-scale CNN and cascaded LSTM to automate the extraction of local features and long-term semantic features. Combined with SMOTE resampling and data augmentation to optimize model training, and a probability threshold policy decision engine, it achieves accurate handling of malicious traffic. The system adopts a five-layer hierarchical architecture, with highly cohesive and loosely coupled modules that can be deployed in an engineered manner. This invention improves the detection capability against obfuscated attacks and unknown attacks, while reducing false positive rates and operational costs, making it suitable for malicious traffic identification scenarios in Web application firewalls.

A system and method that automates trending data collection and timeseries predictions based on a coordinated system of emerging topics across social media, forums, news, media, search engine, web traffic, and other data sources. Using machine learning and natural language processing, trending data counts are cross referenced across each platform to inform representative conversational data collection, cultural classification, and timeseries predictions in order to identify emerging trends and predict trend trajectory over time. User interfaces provided by the system may be used to aid in evaluating emerging cultural trends as they may relate to business activity, law enforcement, and financial and other personal decisions, for example. The system may provide such data based on upon user configured searches that might focus the results on topics such as key consumer, economic, or political topics.

The invention discloses a dark network traffic classification method based on a gradient boosting tree. The method comprises the following steps: constructing a feature matrix and a category matrix according to an original dark web traffic data set; initializing a category weight vector and a cumulative prediction confidence value matrix; introducing a category weight item in front of a loss function item of the XGBoost objective function as a multiplication item, and constructing an initial XGBoost objective function based on an initial category weight vector and an initial cumulative prediction confidence value matrix; based on the initial XGBoost objective function, constructing an initial gradient boosting tree; according to the error rate of each category in the previous round, the category weight is dynamically updated and adjusted to update the XGBoost target function, so that the gradient boosting tree is iteratively constructed until the number of iteration rounds reaches a preset value, and the target gradient boosting tree is finally obtained; and classifying the current dark network traffic based on the target gradient boosting tree. According to the method, a dynamic adjustment mechanism is adopted, so that the model can adaptively increase the attention on the difficult-to-classify categories, and the learning progress of each category is continuously balanced in the training process.

The invention relates to the technical field of flow analysis and early warning, and the method comprises the steps that network flow collected by a flow analysis and early warning system is detected and analyzed through an intrusion detection engine and a behavior detection engine, and a platform provides four data security exchange channels and supports intrusion detection and behavior detection. According to the method, a gene detection technology is used for detecting a variety of a known threat, an intelligent detection technology is combined to prevent escape and avoidance, and a host behavior and a network behavior of a malicious code in a sandbox are deeply analyzed to detect an unknown threat. The intrusion detection engine can sense intrusion in content, environment and application layers. The behavior detection engine carries out file restoration and metadata extraction on traffic, the restored file carries out static detection on known threats through a built-in anti-virus engine, artificial intelligence engine detection is carried out, and malicious encrypted traffic, dark network traffic, hidden tunnels and the like can be detected.

The invention relates to the technical field of information security, and provides a few-sample multi-label website fingerprint identification method and device. The method comprises the following steps: acquiring single-label website traffic data, and constructing multi-label website traffic synthetic data based on the single-label website traffic data; pre-training a teacher model based on the multi-label website traffic synthetic data, wherein the pre-trained teacher model can extract universal features of the multi-label website traffic; obtaining few-sample traffic data of the target multi-label website, and based on the few-sample traffic data and the teacher model, performing fine tuning on the student model through comparative distillation to obtain a target student model of the target multi-label website; and based on the target student model and a plurality of historical samples with similar to-be-identified multi-label website flow data features, carrying out fusion to obtain a multi-label website fingerprint identification result. According to the few-sample multi-label website fingerprint identification method and device provided by the invention, the multi-label website identification capability of the model in a few-sample environment can be improved.

A system for managing web traffic comprising a meta control operating on a first processor having a first control interface and configured to generate a request for content and to transmit the request for content over a digital data network to a meta control server. The meta control server operating on a second processor and configured to receive the request for content and to select data for one or more second control interfaces as a function of data associated with the first control interface and to transmit the data for the one or more second control interfaces over the digital data network to the first control interface. The first control interface displays the data for the one or more second control interfaces and monitors user activity associated with the data.

The present disclosure provides various systems, methods, and devices that can be used to defend against distributed denial of service (DDoS) attacks. In one aspect, the present disclosure provides a method for defending against DDoS attacks that can be implemented by a web server. The web server can generate indirect authentication credentials (IACs) based on authentication credentials associated with a user account and share the IACs with an intermediary server. The intermediary server can be, for example, an Internet server provider server or a proxy server. The web server can further detect anomalies based on web traffic directed to the web server and instruct the intermediary server to verify subsequent web traffic directed to the web server based on the IACs. Thereafter, the web server can receive, via the intermediary server, the verified web traffic directed to the web server.