Mobile equipment intelligent network monitoring and privacy early warning system and application method thereof
Through DPI deep packet detection and machine learning technology, combined with intelligent analysis engine and real-time early warning module, the problem that traditional monitoring means cannot respond to security threats in real time is solved, intelligent network monitoring and privacy warning of mobile devices are realized, and in-depth threat identification is provided. and personalized early warning improve the real-time and accuracy of the monitoring system.
Patent Information
- Application Number
- CN202411426758.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-14
- Publication Date
- 2025-01-14
AI Technical Summary
Traditional mobile device network monitoring methods cannot respond to potential security threats and privacy leaks in real time, rely on passive logging, and lack real-time threat processing capabilities.
DPI deep packet detection technology is used to combine machine learning and intelligent analysis engine to capture data packets through data capture modules, intelligent analysis engine performs deep packet detection, behavior modeling module establishes behavior baseline, real-time early warning module triggers alarms, and report generation module provides detailed reports , realize intelligent network monitoring and privacy warning for mobile devices.
Realize real-time threat identification and privacy leakage of mobile devices, improve the accuracy of early warnings and the intelligibility of reporting, can deeply analyze encrypted data packets, provide in-depth packet detection and AI to identify potential threats, and build personalized Behavioral baseline, support instant feedback and decision support.
Abstract
Description
Technical Field
[0001] The present invention relates to the field of network security and privacy protection, and more specifically to an intelligent network monitoring and privacy early warning system for mobile devices. Background Art
[0002] Given the widespread use of mobile Internet, smartphones and tablets have become important carriers of personal and business activities. However, applications may become the source of privacy leaks due to design flaws, malicious code or improper data processing. Traditional monitoring methods mostly rely on passive logging and post-audit, which is insufficient for real-time threat response. Summary of the invention
[0003] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a mobile device intelligent network monitoring and privacy warning system that utilizes DPI deep packet inspection, data packet analysis, behavior recognition algorithms and machine learning technology to ensure the security of user information on mobile platforms.
[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a mobile device intelligent network monitoring and privacy warning system, comprising:
[0005] A data capture module, responsible for capturing data packets from a network interface of a mobile device;
[0006] The intelligent analysis engine is used to analyze data packets using DPI deep packet inspection technology to identify behaviors such as sensitive information transmission and abnormal network requests.
[0007] As a further improvement of the present invention, it also includes:
[0008] Behavior Modeling Module: This behavior modeling module establishes a normal behavior baseline based on historical data of users and applications, and identifies abnormal behaviors through machine learning technology.
[0009] As a further improvement of the present invention, it also includes:
[0010] The real-time warning module is connected to the intelligent analysis engine and the behavior modeling module to trigger a warning immediately and send an alert to the user or administrator once a potential security threat or privacy leak is detected.
[0011] As a further improvement of the present invention, it also includes:
[0012] The report generation module communicates with the intelligent analysis engine and the behavior modeling module to provide detailed analysis reports, including risk assessment, event details and recommended actions.
[0013] As an application method of the above system, the following steps are included:
[0014] Step 1, the data capture module uses libpcap-compatible libnettrace to monitor and capture all data packets entering and leaving the mobile device through the network interface;
[0015] Step 2: The intelligent analysis engine uses libpcap or similar libraries to parse various network protocols in the data packet, conducts in-depth analysis of the data packet content through Snort, and uses regular expressions and string matching algorithm technology to find sensitive information.
[0016] As a further improvement of the application method, it also includes:
[0017] Step three: The behavior modeling module continuously collects users' network activity data to obtain time series features, traffic features, protocol features, and behavior features, extracts and transforms them according to the selected features, and then divides the data into training sets, validation sets, and test sets. It uses clustering algorithms and anomaly detection algorithms to identify abnormal behaviors. It also uses methods such as grid search or random search to adjust model parameters to obtain the best performance, uses cross-validation to evaluate the generalization ability of the model, and optimizes the model.
[0018] As a further improvement of the application method, it also includes:
[0019] In step 4, the behavior modeling module determines whether the behavior constitutes a security threat or privacy leakage based on the machine learning model. Specifically, it makes a judgment on the access behavior by comparing the machine model with the information resource library, and takes different actions according to different pre-set policy configurations.
[0020] As a further improvement of the application method, it also includes:
[0021] Step 5: Once the real-time warning module detects a high risk, it can implement one or more warning strategies by setting strategies, support push notifications in the app notification bar, send SMS and emails, and support reporting to designated services.
[0022] The beneficial effects of the present invention, compared with the traditional network monitoring system, can effectively achieve the following beneficial effects through the settings of the data capture module, the intelligent analysis engine, the behavior modeling module, the real-time warning module and the report generation module:
[0023] 1. Deep packet inspection and intelligent analysis: It can deeply analyze encrypted data packets and identify potential threats by combining AI technology.
[0024] 2. Real-time warning mechanism: provides instant feedback and enables rapid response when security incidents occur.
[0025] 3. User behavior modeling: Build personalized behavior baselines through machine learning to improve the accuracy of early warnings. Comprehensive report generation: Provide easy-to-understand reports to help users and managers understand security status and make decisions. DETAILED DESCRIPTION
[0026] The present invention will be further described in detail with reference to the given embodiments below.
[0027] A mobile device intelligent network monitoring and privacy warning system of this embodiment includes:
[0028] Data capture module: responsible for capturing data packets from the network interface of the mobile device.
[0029] Intelligent Analysis Engine: Uses DPI deep packet inspection technology to analyze data packets and identify behaviors such as sensitive information transmission and abnormal network requests. DPI technology can inspect the payload of data packets and can therefore be used to identify sensitive information outside of encrypted traffic.
[0030] Behavior modeling module: Establish a normal behavior baseline based on historical data of users and applications, and identify abnormal behaviors through machine learning technology.
[0031] Real-time warning module: Once a potential security threat or privacy leak is detected, an early warning is triggered immediately and an alert is sent to the user or administrator.
[0032] Report generation module: Provides detailed analysis reports including risk assessment, event details and recommended actions.
[0033] On the other hand, this embodiment provides an application method of the above-mentioned mobile device intelligent network monitoring and privacy warning system, including the following steps:
[0034] 1. Data collection: Use libpcap-compatible libnettrace to listen and capture all packets entering and leaving the mobile device through the network interface.
[0035] 2. Data parsing: Use libpcap or similar libraries to parse various network protocols (such as TCP / UDP / IP / HTTP, etc.) in data packets, conduct in-depth analysis of data packet contents through Snort, and use regular expressions and string matching algorithm techniques to find sensitive information.
[0036] 3. Behavior analysis: Compare the information in the data packet with the preset behavior model to identify anomalies. By continuously collecting users' network activity data, obtain time series features, traffic features, protocol features, and behavior features, and extract and transform them according to the selected features. Divide the training set, validation set, and test set data, and use clustering algorithms (such as K-means, DBSCAN) and anomaly detection algorithms (such as Isolation Forest, One-Class SVM) to identify abnormal behaviors. Use methods such as grid search or random search to adjust model parameters for optimal performance, use cross-validation to evaluate the generalization ability of the model, and optimize the model.
[0037] 4. Intelligent decision-making: Based on machine learning models, determine whether the behavior constitutes a security threat or privacy leakage. The machine model compares the information resource library to make a judgment on the access behavior and takes different actions according to different pre-set policy configurations.
[0038] 5. Early warning and response: Once a high risk is detected, one or more early warning strategies can be implemented by setting strategies, which can support push notifications in the app notification bar, send SMS and email, and support reporting to designated services
[0039] To sum up, the mobile device intelligent network monitoring and privacy warning system and its application method of this embodiment propose an intelligent network monitoring and privacy warning system, which combines deep packet inspection, behavior analysis, machine learning and real-time warning technology to achieve comprehensive monitoring of mobile application data transmission and timely discover potential security vulnerabilities and privacy leakage risks.
[0040] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.
Claims
1. A mobile device intelligent network monitoring and privacy warning system, characterized by: include: A data capture module, responsible for capturing data packets from a network interface of a mobile device; The intelligent analysis engine is used to analyze data packets using DPI deep packet inspection technology to identify behaviors such as sensitive information transmission and abnormal network requests.
2. The mobile device intelligent network monitoring and privacy warning system according to claim 1 is characterized by: Also includes: Behavior Modeling Module: This behavior modeling module establishes a normal behavior baseline based on historical data of users and applications, and identifies abnormal behaviors through machine learning technology.
3. The mobile device intelligent network monitoring and privacy warning system according to claim 2 is characterized by: Also includes: The real-time warning module is connected to the intelligent analysis engine and the behavior modeling module to trigger a warning immediately and send an alert to the user or administrator once a potential security threat or privacy leak is detected.
4. The mobile device intelligent network monitoring and privacy warning system according to claim 3 is characterized by: Also includes: The report generation module communicates with the intelligent analysis engine and the behavior modeling module to provide detailed analysis reports, including risk assessment, event details and recommended actions.
5. A network monitoring and privacy warning method using the mobile device intelligent network monitoring and privacy warning system of claim 4, characterized in that: The steps include: Step 1, the data capture module uses libpcap-compatible libnettrace to monitor and capture all data packets entering and leaving the mobile device through the network interface; Step 2: The intelligent analysis engine uses libpcap or similar libraries to parse various network protocols in the data packet, conducts in-depth analysis of the data packet content through Snort, and uses regular expressions and string matching algorithm technology to find sensitive information.
6. The network monitoring and privacy warning method of the mobile device intelligent network monitoring and privacy warning system according to claim 5, characterized in that: Also includes: Step three: The behavior modeling module continuously collects users' network activity data to obtain time series features, traffic features, protocol features, and behavior features, extracts and transforms them according to the selected features, and then divides the data into training sets, validation sets, and test sets. It uses clustering algorithms and anomaly detection algorithms to identify abnormal behaviors. It also uses grid search or random search methods to adjust model parameters to obtain the best performance, uses cross-validation to evaluate the generalization ability of the model, and optimizes the model.
7. The network monitoring and privacy warning method of the mobile device intelligent network monitoring and privacy warning system according to claim 6, characterized in that: Also includes: In step 4, the behavior modeling module determines whether the behavior constitutes a security threat or privacy leakage based on the machine learning model. Specifically, it makes a judgment on the access behavior by comparing the machine model with the information resource library, and takes different actions according to different pre-set policy configurations.
8. The network monitoring and privacy warning method of the mobile device intelligent network monitoring and privacy warning system according to claim 7, characterized in that: Also includes: Step 5: Once the real-time warning module detects a high risk, it can implement one or more warning strategies by setting strategies, support push notifications in the app notification bar, send SMS and emails, and support reporting to designated services.