Fraud detection system at base stations

Abstract

The present invention relates to a system (1) which enables base station energy data in the form of cost and data traffic to be analyzed via random forest machine learning techniques, base stations suspected of fraud in the analysis to be detected and the operation to be notified.

Description

[0001] FRAUD DETECTION SYSTEM AT BASE STATIONS

[0002] Technical Field

[0003] The present invention relates to a system which enables base station energy data in the form of cost and data traffic to be analyzed via random forest machine learning techniques, base stations suspected of fraud in the analysis to be detected and the operation to be notified.

[0004] Background of the Invention

[0005] Energy fraud at base stations is a serious problem that threatens the security of mobile communications infrastructure. This type of fraud involves hiding or manipulating the actual amount of electrical energy consumed by stations, usually by infiltrating the systems of energy providers. Fraudsters, by using fake meters or software, make energy consumption appear lower than it actually is, thereby underpaying energy providers. Such fraud can lead to huge financial losses for both energy companies and consumers and weaken the security of the energy infrastructure in the long term. Moreover, such illegal activities may cause energy prices to rise and reduce the overall efficiency of the system by increasing the financial burden on the energy sector.

[0006] For this reason, it is understood that there is a need for a system which enables base station energy data in the form of cost and data traffic to be analyzed via random forest machine learning techniques, base stations suspected of fraud in the analysis to be detected and the operation to be notified.

[0007] The Chinese patent document no. CN117708552, an application included in the state of the art, discloses a power station operation and maintenance data via real- time monitoring method based on edge calculation. The said invention describes a power station operation and maintenance data real-time monitoring method based on edge calculation, and this method belongs to the technical field of operation and maintenance monitoring. The method comprises the steps of collecting key operation and maintenance data of power generation equipment by using Internet of Things (loT) technology and carrying out the primary processing of this key operation and maintenance data through edge calculation; uploading the key operation and maintenance data subjected to primary processing to a data center and subjecting it to secondary processing; the data center creates a prediction model by using a random forest algorithm and establishes a prediction model based on the key operation and maintenance data of the power generation equipment subjected to secondary processing; with the output of the real-time monitoring and prediction model, the state of the power generation equipment is monitored in real time, whether abnormity occurs or not is judged, and if abnormity is detected, an early warning and response program is automatically triggered. Key data are collected in real time, potential faults are quickly recognized and responded, thereby shortening the duration of equipment failures. Through automatic and intelligent processing, pre-fault prevention capability is improved, and maintenance decisions are optimized, thereby increasing overall operational efficiency and reducing the cost of operation and maintenance.

[0008] Summary of the Invention

[0009] An object of the present invention is to realize a system developed with the aim of enabling base station energy data in the form of cost and data traffic to be analyzed via random forest machine learning techniques, base stations suspected of fraud in the analysis to be detected and the operation to be notified.

[0010] Detailed Description of the Invention “Fraud Detection System at Base Stations” realized to fulfil the objective of the present invention is shown in the figure attached, in which:

[0011] Figure l is a schematic view of the inventive system.

[0012] The components illustrated in the figure are individually numbered, where the numbers refer to the following:

[0013] 1. System

[0014] 2. Database

[0015] 3. Server

[0016] The inventive system (1) developed with the aim of enabling base station energy data in the form of cost and data traffic to be analyzed via random forest machine learning techniques, base stations suspected of fraud in the analysis to be detected and the operation to be notified comprises at least one database (2) which is configured to keep a record of base station cost and traffic data; and at least one server (3) which is configured to pre-process base station cost and traffic data; to enable cost and traffic matching anomaly analysis to be applied via random forest algorithm; to enable the data to be labeled according to the analysis results; and to enable the records detected as anomalies to be transmitted to the relevant teams.

[0017] The database (2) included in the inventive system (1) is configured to establish communication and exchange data with the server (3) by using any communication protocol. The database (2) is configured to receive base station cost and traffic data from various sources by using any communication protocol.

[0018] The server (3) included in the inventive system (1) is configured to establish communication and exchange data with the database (2) by using any communication protocol. The server (3) is configured to enable the database (2) in which the table containing the base station energy cost and traffic density is located to be accessed and the said data to be analyzed. The server (3) is configured to enable the data used during the analysis to be between certain periods. The server (3) is configured to enable the data kept recorded in the database (2) to be pre- processed, the data to be converted into a format that the random forest technique can process by processing it, and the normalization of the data to be carried out. The server (3) is configured to enable the random forest technique to be applied to the accessed data, a model to be trained through features by using the random forest algorithm, the trained model to learn energy costs and to identify out-of-norm behaviors. The server (3) is configured to enable grading for the anomalous behaviors detected after the application of the random forest technique to be performed and the base stations identified according to the grading to be labeled as anomalies. The server (3) is configured to enable values labeled as anomalies to be transmitted to the relevant units through a queue structure. The server (3) is configured to enable the results containing suspicion of fraud as a result of the analysis to be assigned to a queue structure as a message and the message contents processed in the queue structure to be transmitted to the relevant service provider personnel by any means of communication.

[0019] Industrial Application of the Invention

[0020] By means of the inventive system (1), it is enabled to analyze base station energy data in the form of cost and data traffic via random forest machine learning techniques, to detect base stations suspected of fraud in the analysis and to notify the operation.

[0021] Within these basic concepts; it is possible to develop various embodiments of the inventive “Fraud Detection System (1) at Base Stations”; the invention cannot be limited to examples disclosed herein and it is essentially according to claims.

Claims

CLAIMS1. A system (1) which enables base station energy data in the form of cost and data traffic to be analyzed via random forest machine learning techniques, base stations suspected of fraud in the analysis to be detected and the operation to be notified; comprising at least one database (2) which is configured to keep a record of base station cost and traffic data; and characterized by at least one server (3) which is configured to pre-process base station cost and traffic data; to enable cost and traffic matching anomaly analysis to be applied via random forest algorithm; to enable the data to be labeled according to the analysis results; and to enable the records detected as anomalies to be transmitted to the relevant teams.

2. A system (1) according to Claim 1 ; characterized by the database (2) which is configured to establish communication and exchange data with the server (3) by using any communication protocol.

3. A system (1) according to Claim 1 or 2; characterized by the database (2) which is configured to receive base station cost and traffic data from various sources by using any communication protocol.

4. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to establish communication and exchange data with the database (2) by using any communication protocol.

5. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable the database (2) in which the table containing the base station energy cost and traffic density is located to be accessed and the said data to be analyzed.

6. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable the data used during the analysis to be between certain periods.

7. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable the data kept recorded in the database (2) to be pre-processed, the data to be converted into a format that the random forest technique can process by processing it, and the normalization of the data to be carried out.

8. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable the random forest technique to be applied to the accessed data, a model to be trained through features by using the random forest algorithm, the trained model to learn energy costs and to identify out- of-norm behaviors.

9. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable grading for the anomalous behaviors detected after the application of the random forest technique to be performed and the base stations identified according to the grading to be labeled as anomalies.

10. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable values labeled as anomalies to be transmitted to the relevant units through a queue structure.

11. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable the results containing suspicion of fraud as a result of the analysis to be assigned to a queue structure as a message and the message contents processed in the queue structure to be transmitted to the relevant service provider personnel by any means of communication.

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