DPI (Deep Packet Inspection) data-based anti-electricity fraud data early warning method

Abstract

The invention relates to an anti-electricity fraud data early warning method based on DPI data, and belongs to the technical field of anti-electricity fraud data early warning. The method comprises the following steps: S1, data acquisition: monitoring network traffic through a network detection technology by using a DPI technology, and acquiring data packet content and traffic characteristics; s2, data processing: the data server performs cleaning, duplicate removal and format conversion processing on the acquired data; s3, feature extraction: extracting related features from the processed data; s4, model training: training an anti-electricity fraud model which can be trained based on fraud features in historical data to identify similar behavior patterns; s5, real-time monitoring is carried out; s6, performing anomaly detection and early warning; and S7, data analysis and feedback. The invention provides an anti-electricity fraud data early warning method based on DPI (Deep Packet Inspection) data, which combines network flow analysis and an advanced machine learning technology, and can effectively identify and prevent telecommunication fraud and improve network security through continuous data acquisition, processing, analysis and feedback.

Description

Technical Field

[0001] The present invention relates to an anti-fraud data early warning method based on DPI data, belonging to the technical field of anti-fraud data early warning. Background Art

[0002] DPI (Deep Packet Inspection) call data usually refers to network communication data collected through network detection technology. In the field of network security, DPI data collection technology helps security experts gain in-depth understanding of abnormal behaviors and threats in network traffic. By analyzing network traffic data, potential network security threats can be discovered in a timely manner, such as malware activities, unauthorized access, etc., so that corresponding security measures can be taken.

[0003] The anti-telecom fraud data warning method based on DPI (Deep Packet Inspection) data involves using deep packet inspection technology to monitor and analyze network traffic to identify potential telecommunications fraud. However, when conducting data identification and warning of telecommunications fraud, due to the changes in fraud methods and the complexity of data, it is often difficult to continuously collect, process, analyze and feedback data, and network security needs to be improved. Summary of the invention

[0004] In view of this, the present invention provides an anti-telecom fraud data early warning method based on DPI data, which combines network traffic analysis and advanced machine learning technology. Through continuous data collection, processing, analysis and feedback, it can effectively identify and prevent telecommunications fraud and improve network security.

[0005] The present invention provides an anti-fraud data early warning method based on DPI data, and the proposed technical solution is: comprising the following steps:

[0006] S1: Data collection: Use DPI technology to monitor network traffic through network detection technology, collect data packet content and traffic characteristics;

[0007] S2: Data processing: The data server cleans, removes duplicates, and converts the collected data;

[0008] S3: Feature extraction: Extract relevant features from the processed data to help identify and distinguish normal and abnormal network behaviors;

[0009] S4: Model training: Train the anti-fraud model. The model can be trained based on the fraud features in historical data to identify similar behavior patterns.

[0010] S5: Real-time monitoring: Deploy the trained model in the network monitoring system to analyze the current network traffic data in real time to detect whether there are abnormal behaviors similar to known fraud patterns;

[0011] S6: Anomaly detection and warning: Set thresholds and rules to trigger the warning mechanism when abnormal behavior or potential fraud activities are detected, and store their behavior in the National Anti-Fraud Center database;

[0012] S7: Data analysis and feedback: Conduct in-depth analysis of detected fraudulent activities to continuously optimize models and detection rules.

[0013] Furthermore, in step S1: the content and traffic characteristics include source address, destination address, protocol type, and data load information.

[0014] Furthermore, in step S1: when implementing DPI technology, it is necessary to ensure compliance with relevant laws and regulations, and take measures to protect user privacy, and data collection should follow legal and compliant requirements.

[0015] Furthermore, in step S2: a data cleaning tool is used to remove invalid, duplicate or erroneous data records, and an ETL extraction, transformation and loading process is used to format the data to conform to a standard format.

[0016] Furthermore, in step S3: relevant features extracted include data sender, traffic pattern, access frequency, data packet size, connection duration, fraud history, communication behavior, and consumption behavior of the data sender.

[0017] Furthermore, in step S4: one or both of machine learning or deep learning technologies are used to train the anti-fraud model, and the algorithm used is one of a decision tree, a support vector machine, and a neural network.

[0018] Furthermore, in step S5: a similarity comparison is performed based on the fraud features in the historical data and the relevant features extracted from the data, and the current network traffic data is analyzed in real time.

[0019] Furthermore, in step S6: the early warning adopts at least one of alarm notification, traffic isolation, and log recording.

[0020] Furthermore, in step S7: data is summarized into PPT documents, Excel tables, and Word documents to convert data into understandable and actionable insights, thereby helping to adjust model parameters and update feature extraction methods.

[0021] Beneficial effects of the present invention:

[0022] The present invention provides an anti-telecom fraud data early warning method based on DPI data, which combines network traffic analysis and advanced machine learning technology. Through continuous data collection, processing, analysis and feedback, it can effectively identify and prevent telecommunications fraud and improve network security. DETAILED DESCRIPTION

[0023] The preferred embodiments of the present invention will be described in detail below.

[0024] The present invention provides an anti-fraud data early warning method based on DPI data, which comprises the following steps:

[0025] S1: Data collection: Use DPI technology to monitor network traffic through network detection technology, collect data packet content and traffic characteristics. When implementing DPI technology, it is necessary to ensure compliance with relevant laws and regulations and take measures to protect user privacy. Data collection should follow legal and compliant requirements. Data sources include but are not limited to network traffic, communication logs, DNS requests, etc.

[0026] S2: Data processing: The data server cleans, removes duplicates, and converts the collected data to remove noise data, such as irrelevant network traffic or data with incorrect formats, deletes duplicate data records, ensures the uniqueness of each record in the data set, and converts the raw data into a standardized format for subsequent processing and analysis;

[0027] S3: Feature extraction: Extract relevant features from the processed data to help identify and distinguish normal and abnormal network behaviors;

[0028] S4: Model training: Train the anti-fraud model, mark the normal behavior and fraudulent behavior in the historical data as the basic data set for model training. The model can be trained based on the fraud features in the historical data to identify similar behavior patterns.

[0029] S5: Real-time monitoring: Deploy the trained model in the network monitoring system to analyze the current network traffic data in real time to detect whether there are abnormal behaviors similar to known fraud patterns;

[0030] S6: Anomaly detection and warning: Set thresholds and rules to trigger the warning mechanism when abnormal behavior or potential fraud activities are detected, and store their behavior in the National Anti-Fraud Center database for further analysis and tracking;

[0031] S7: Data Analysis and Feedback: Conduct in-depth analysis of detected fraudulent activities, conduct detailed analysis of detected fraudulent activities, evaluate their nature, scope and impact, analyze attackers’ behavior patterns, victim characteristics, attack methods, etc. These analysis results are used to optimize models and update detection rules to continuously optimize models and detection rules.

[0032] As an optional embodiment, in step S1: the content and traffic characteristics include source address, destination address, protocol type, and data load information, through which the communication mode and data flow in the network can be understood.

[0033] As an optional embodiment, in step S2: a data cleaning tool is used to remove invalid, duplicate or erroneous data records, and the data is formatted using an ETL extraction, conversion, and loading process to remove irrelevant or redundant information to ensure the accuracy and consistency of the data. The processing steps include deduplication (deleting duplicate records), format conversion (converting data into a format suitable for analysis), and other operations to make it conform to the standard format.

[0034] As an optional embodiment, in step S3: extracting relevant features including the data sender, traffic pattern, access frequency, data packet size, connection duration, the data sender's fraud history, communication behavior, and consumption behavior helps the model understand the difference between normal traffic and abnormal traffic.

[0035] As an optional embodiment, in step S4: one or both of machine learning or deep learning technologies are used to train the anti-fraud model, and the algorithm used is one of decision tree, support vector machine, and neural network. During the training process, the model will adjust parameters to improve the accuracy of identifying fraudulent activities.

[0036] As an optional embodiment, in step S5: a similarity comparison is performed based on the fraud features in the historical data and the relevant features extracted from the data, and the current network traffic data is analyzed in real time. Real-time monitoring helps to detect potential fraud activities in a timely manner.

[0037] As an optional embodiment, in step S6: the early warning adopts at least one of alarm notification, traffic isolation, and log recording.

[0038] As an optional embodiment, in step S7: by summarizing data for PPT documents, Excel tables, and Word documents to convert data into understandable and actionable insights, it helps to adjust model parameters and update feature extraction methods to improve detection accuracy.

[0039] Working principle:

[0040] S1: Data collection: Use DPI technology to monitor network traffic through network detection technology, collect data packet content and traffic characteristics. When implementing DPI technology, it is necessary to ensure compliance with relevant laws and regulations and take measures to protect user privacy. Data collection should follow legal and compliant requirements. Data sources include but are not limited to network traffic, communication logs, DNS requests, etc. Content and traffic characteristics include source address, destination address, protocol type, data load information, through which we can understand the communication mode and data flow in the network; S2: Data processing: The data server cleans, removes duplicates, and converts the collected data. Data cleaning tools are used to remove invalid, duplicated or erroneous data records, such as irrelevant network traffic or erroneous format data, to ensure the uniqueness of each record in the data set. ETL extraction, transformation, and loading processes are used to format the data to remove irrelevant or redundant information, and convert the raw data into a standardized format for subsequent processing and analysis, to ensure the accuracy and consistency of the data, and to make it conform to the standard format; S3: Feature extraction: Extract relevant features from the processed data to help identify and distinguish normal and abnormal network behaviors. Extracted relevant features include data sender, traffic pattern, access frequency, packet size, connection The duration, the fraud history, communication behavior, and consumption behavior of the data sender can help the model understand the difference between normal traffic and abnormal traffic; S4: Model training: Train the anti-fraud model, mark the normal behavior and fraud behavior in the historical data, and use it as the basic data set for model training. The model can be trained based on the fraud features in the historical data to identify similar behavior patterns. The anti-fraud model is trained using one or two of the machine learning or deep learning technologies. The algorithm used is one of the decision tree, support vector machine, and neural network. During the training process, the model will adjust the parameters to improve the accuracy of identifying fraud activities; S5: Real-time monitoring: Deploy the trained model in the network monitoring system, perform a similarity comparison based on the fraud features in the historical data and the relevant features extracted from the data, and analyze the current network traffic data in real time. Real-time monitoring helps to detect potential fraud activities in a timely manner and detect whether there are abnormal behaviors similar to known fraud patterns; S6: Anomaly detection and early warning: Set thresholds and rules. When abnormal behavior or potential fraud activities are detected, the early warning mechanism is triggered. The early warning uses at least one of alarm notification, traffic isolation, and log records, and stores its behavior in the database of the National Anti-Fraud Center for further analysis and tracking;S7: Data analysis and feedback: Conduct in-depth analysis of detected fraudulent activities, conduct detailed analysis of detected fraudulent activities, evaluate their nature, scope and impact, analyze the attacker's behavior patterns, victim characteristics, attack methods, etc., and summarize data for PPT documents, Excel tables, and Word documents to transform data into understandable and actionable insights. These analysis results are used to optimize models and update detection rules to continuously optimize models and detection rules, help adjust model parameters and update feature extraction methods to improve detection accuracy. ;

[0041] The above-mentioned anti-telecom fraud data warning method based on DPI data combines network traffic analysis and advanced machine learning technology. Through continuous data collection, processing, analysis and feedback, it can effectively identify and prevent telecommunications fraud and improve network security.

[0042] The present invention and its implementation methods are described above, and such description is not restrictive, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design structures and embodiments similar to the technical solution without creativity, which should all fall within the protection scope of the present invention.

Claims

1. An anti-fraud data early warning method based on DPI data, characterized by: The following steps are involved: S1: Data collection: Use DPI technology to monitor network traffic through network detection technology, collect data packet content and traffic characteristics; S2: Data processing: The data server cleans, removes duplicates, and converts the collected data; S3: Feature extraction: Extract relevant features from the processed data to help identify and distinguish normal and abnormal network behaviors; S4: Model training: Train the anti-fraud model. The model can be trained based on the fraud features in historical data to identify similar behavior patterns. S5: Real-time monitoring: Deploy the trained model in the network monitoring system to analyze the current network traffic data in real time to detect whether there are abnormal behaviors similar to known fraud patterns; S6: Anomaly detection and warning: Set thresholds and rules to trigger the warning mechanism when abnormal behavior or potential fraud activities are detected, and store their behavior in the National Anti-Fraud Center database; S7: Data analysis and feedback: Conduct in-depth analysis of detected fraudulent activities to continuously optimize models and detection rules.

2. According to claim 1, the anti-fraud data early warning method based on DPI data is characterized by: In step S1: content and traffic characteristics include source address, destination address, protocol type, and data load information.

3. The anti-fraud data early warning method based on DPI data according to claim 1 is characterized by: In step S1: When implementing DPI technology, it is necessary to ensure compliance with relevant laws and regulations, take measures to protect user privacy, and collect data in accordance with legal and compliant requirements.

4. The anti-fraud data early warning method based on DPI data according to claim 1 is characterized by: In step S2: a data cleaning tool is used to remove invalid, duplicate or erroneous data records, and an ETL extraction, conversion and loading process is used to format the data to conform to a standard format.

5. The anti-fraud data early warning method based on DPI data according to claim 1 is characterized by: In step S3: extracting relevant features including data sender, traffic pattern, access frequency, data packet size, connection duration, data sender's fraud history, communication behavior, and consumption behavior.

6. The anti-fraud data early warning method based on DPI data according to claim 1 is characterized by: In step S4: one or both of machine learning and deep learning technologies are used to train the anti-fraud model, and the algorithm used is one of decision tree, support vector machine, and neural network.

7. The anti-fraud data early warning method based on DPI data according to claim 1 is characterized by: In step S5: a similarity comparison is performed based on the fraud features in the historical data and the relevant features extracted from the data, and the current network traffic data is analyzed in real time.

8. The anti-fraud data early warning method based on DPI data according to claim 1 is characterized by: In step S6: the early warning adopts at least one of alarm notification, traffic isolation, and log recording.

9. The anti-fraud data early warning method based on DPI data according to claim 1 is characterized by: In step S7: data is summarized into PPT documents, Excel tables, and Word documents to convert data into understandable and actionable insights, thereby helping to adjust model parameters and update feature extraction methods.