CN-121998644-A - Data transaction tracking and early warning method and system based on artificial intelligence

CN121998644ACN 121998644 ACN121998644 ACN 121998644ACN-121998644-A

Abstract

The invention discloses a data transaction tracking and early warning method and system based on artificial intelligence, and relates to the field of artificial intelligence data transaction tracking. The method comprises the steps of recording and collecting data transaction information through an HTTP request head, a client SDK, a proxy server and a database of a user, processing and analyzing the collected data transaction information to extract data transaction characteristics, analyzing the data transaction characteristics by adopting an isolated forest algorithm to identify normal data transaction information and abnormal data transaction information, generating an alarm when the abnormal data transaction information is identified, and recording the data transaction characteristics of the abnormal data transaction information. Through comprehensive analysis of multidimensional features and an isolated forest algorithm, the safety, the accuracy and the automation level of data transaction monitoring are improved, potential abnormal modes in data transaction can be comprehensively mined, and risks are effectively reduced.

Inventors

TENG SHUHUA
CHEN CHENG
ZHANG RUI
ZHONG YUANYUAN
ZENG ZHUO
LI YAO
GAO FENG
ZHANG FENG
BAO YIPING

Assignees

山东协和学院
湖南大数据交易所有限公司

Dates

Publication Date: 20260508
Application Date: 20251230

Claims (9)

1. The data transaction tracking and early warning method based on artificial intelligence is characterized by comprising the following steps of: Collecting data transaction information through HTTP request header, client SDK, proxy server and database record of user; processing and analyzing the collected data transaction information, and extracting data transaction characteristics; analyzing the data transaction characteristics by adopting an isolated forest algorithm, and identifying normal data transaction information and abnormal data transaction information; when the abnormal data transaction information is identified, an alarm is generated, and the data transaction characteristics of the abnormal data transaction information are recorded.
2. The method of claim 1, wherein collecting data transaction information comprises switching frequency of devices or networks, total number of data transactions of the user, time delay of the data transactions, active time period of the data transactions, size and frequency of the data transactions during the data transactions.
3. The method for tracking and early warning data transaction based on artificial intelligence according to claim 2, wherein the steps of processing and analyzing the collected data transaction information and extracting the data transaction characteristics comprise: filling the missing value, removing the abnormal value, normalizing and normalizing the collected data transaction information; extracting data transaction stability characteristics according to the switching frequency of equipment or network of a user in a certain time range and the time delay of data transaction; extracting an activity period deviation feature according to the activity period of data transaction carried out by the user and the total number of data transaction; and extracting the digital frequency characteristic according to the data size and the transaction frequency of the data transaction performed by the user.
4. The method of claim 3, wherein the data transaction stability characteristics are calculated by the logic: , wherein, As a feature of the stability of the data transaction, For a device or network to switch frequencies, The time delay for the data transaction, For a desired time threshold for a normal transaction, To control the parameters of the time delay on the intensity of the influence of the feature, To control the parameters of the period of influence of the switching frequency of the device on the characteristics, And Is a nonlinear adjustment coefficient.
5. The method of claim 3, wherein the logic for calculating the activity period deviation feature is , wherein, In order for the active period to deviate from the characteristics, Is the first The time of the data transaction is counted, The average point in time of the active period for which the user is conducting a data transaction, For the total number of user data transactions, Is an adjustment factor for controlling the weight of the degree of departure of the active period.
6. The method for tracking and early warning data transaction based on artificial intelligence according to claim 3, wherein the calculation logic of the digital frequency characteristics is as follows , wherein, The characteristic of the digital frequency is that, In order for the data to be of a size, At the maximum value of the data size, For the frequency of the transaction, And To adjust the parameters.
7. The artificial intelligence based data transaction tracking and early warning method according to claim 3, wherein the analysis of data transaction characteristics by using an isolated forest algorithm, and the identification of normal data transaction information and abnormal data transaction information, comprises: Using Min-Max standardization processing to standardize the data transaction characteristics; characterization of data transaction stability after normalization Active period deviation feature Digital frequency characterization Combining the three-dimensional feature matrixes; Training an isolated forest model by using a three-dimensional feature matrix, constructing a plurality of trees on a randomly selected feature subset and a data subset in the training process of the isolated forest, isolating normal data from abnormal data, obtaining an abnormal score by each sample after training, judging whether data transaction is abnormal data transaction according to the abnormal score output by the isolated forest, judging that the data transaction is abnormal when the abnormal score is higher than a preset threshold, and judging that the data transaction is normal when the abnormal score is lower than or equal to the preset threshold.
8. The method of claim 7, wherein the logic for calculating the anomaly score is: , wherein, For the anomaly score of the sample, Is a constant of the standardization of the number of the units, Is a sample The path length required to be isolated, Is a sample Is used to determine the desired path length of the (c) signal, The calculation formula of (2) is , Is the number of trees in an isolated forest, Is a sample In the first place Path length in tree, TS is sample node mutation value, its calculation logic is , For the data subset corresponding to the feature subset when the sample is the root node, Is the root node A subset of data corresponding to the feature subset of the left child node of (c), Is the root node A subset of data corresponding to the feature subset of the right child node of (a).
9. An artificial intelligence based data transaction tracking and early warning system for implementing the artificial intelligence based data transaction tracking and early warning method as claimed in any one of claims 1 to 8, comprising: The data acquisition module acquires data transaction information through HTTP request header of a user, SDK of a client, proxy server and database record; The data processing module is used for processing and analyzing the collected data transaction information and extracting data transaction characteristics; The data analysis module is used for analyzing the data transaction characteristics by adopting an isolated forest algorithm and identifying normal data transaction information and abnormal data transaction information; And the early warning module is used for generating an alarm when the abnormal data transaction information is identified and recording the data transaction characteristics of the abnormal data transaction information.

Description

Data transaction tracking and early warning method and system based on artificial intelligence Technical Field The invention relates to the field of data transaction tracking and early warning, in particular to a data transaction tracking and early warning method and system based on artificial intelligence. Background With the continued development of information technology and the increasing popularity of data transactions, data transactions have become a central element in global economy and internet operation as an important component of modern network applications. Whether an online service platform or an e-commerce website, the security and stability of data transaction are directly related to the reliability of the system, the protection of user privacy and the overall information security. Therefore, how to monitor and prevent abnormal data transaction behavior in real time and ensure normal running of network transaction has become an important research topic in the field of information security. In order to improve the security of data transactions, in recent years, an anomaly detection method based on artificial intelligence is becoming a research hotspot. However, existing detection methods often rely on simple parameter features such as transaction frequency, transaction size, etc. However, these features often do not fully reflect the potential risk in data transactions. More complex features that may be involved in data transactions have not been fully mined, resulting in limited detection accuracy. Therefore, the data transaction tracking and early warning method based on artificial intelligence can effectively combine multidimensional features in the anomaly detection process, and improves the data transaction safety. By introducing an isolated forest algorithm, abnormal transaction identification is performed in large-scale data in a self-adaptive manner, so that manual intervention and fixed mode limitation in the traditional rule method are avoided, a changed transaction mode can be responded quickly, and the real-time performance and accuracy of detection are improved. In addition, by introducing more complex features, such as equipment or network switching frequency, active period deviation and the like, the risk of transaction behaviors can be reflected more comprehensively, and more powerful guarantee is provided for data transaction safety. The method not only improves the safety of data transaction, but also has better universality, can adapt to the actual requirements under different service scenes, can realize automatic early warning response, and effectively reduces the probability of human errors and missed detection. Disclosure of Invention Based on the shortcomings of the prior art, the invention aims to provide a data transaction tracking and early warning method and system based on artificial intelligence so as to solve the technical problems. In order to achieve the purpose, the invention provides the following technical scheme that the data transaction tracking and early warning method based on artificial intelligence comprises the following steps: Collecting data transaction information through HTTP request header, client SDK, proxy server and database record of user; processing and analyzing the collected data transaction information, and extracting data transaction characteristics; analyzing the data transaction characteristics by adopting an isolated forest algorithm, and identifying normal data transaction information and abnormal data transaction information; when the abnormal data transaction information is identified, an alarm is generated, and the data transaction characteristics of the abnormal data transaction information are recorded. The invention is further arranged that the data transaction information is collected including the switching frequency of equipment or network, the total number of data transactions of the user, the time delay of the data transactions, the active time period of the data transactions, the size and frequency of the data transactions during the data transactions of the user. The invention is further configured to process and analyze the collected data transaction information to extract data transaction characteristics, including: filling the missing value, removing the abnormal value, normalizing and normalizing the collected data transaction information; extracting data transaction stability characteristics according to the switching frequency of equipment or network of a user in a certain time range and the time delay of data transaction; extracting an activity period deviation feature according to the activity period of data transaction carried out by the user and the total number of data transaction; and extracting the digital frequency characteristic according to the data size and the transaction frequency of the data transaction performed by the user. The invention is further arranged as a data transaction stability feature, th