FAKE BASE STATION DETECTION SYSTEM IN 6G NETWORKS USING CHANNEL CHARACTERISTICS AND RF FINGERPRINT ANALYSIS

Abstract

The invention relates to a system that ensures the security of the connection between user equipment and the base station in 6G networks. The system includes an RF Fingerprint Analysis Module (1), a Channel State Information (CSI) Processing Unit (2), a Timing Deviation and Doppler Analyzer (3), a Security Decision Mechanism (4), a Dynamic Threshold Update Unit (5), and a Network Management and Alarm System (6). The system calculates a security score by combining RF fingerprint, channel state, and timing data and detects false base stations based on this score. Thanks to the Dynamic Threshold Update Unit (5), false alarm rates are reduced by adapting to environmental conditions. The Network Management and Alarm System (6) enhances network security by reporting detected threats.

Description

1 TARIFF FAKE BASE STATIONS IN 6G NETWORKS USING CHANNEL CHARACTERISTICS AND RF FINGERPRINT ANALYSIS. STATION DETECTION SYSTEM Technical Area 5 The invention relates to the connection between user equipment and a base station in 6G mobile communication networks. It relates to a system that ensures security. In particular, the invention concerns the physical layer parameters. using a system that detects fake base stations, identity impersonations, and unauthorized access attempts. It is related to the security verification system. 10 State of the Art Current mobile network security systems are primarily based on SIM card-based authentication and They are based on encryption protocols. However, these systems involve 15 steps at the physical layer. They are vulnerable to attacks. Fake base stations can impersonate a legitimate base station. By impersonating user information, they are able to connect user equipment to their own. Existing techniques that involve easily manipulated parameters such as signal strength or cell identity This situation allows attackers to increase signal strength and thus endanger user equipment. This allows for deception and monitoring of data traffic. Furthermore, current systems use channel 20. instantaneous changes in conditions and RF hardware characteristics for security verification. It does not use it. Purpose of the Invention The invention improves connection security between user equipment and base stations in 6G networks. It aims to provide this through physical layer parameters. The main purpose of the invention is: By analyzing the unique RF fingerprint and channel characteristics of each base station, it can detect fakes. The system detects base stations. It analyzes signal timing deviation, channel status information, and It creates a security score by combining Doppler behavior. This allows credentials to be verified using 30... Even if they are correct, base stations with mismatched physical characteristics are immediately detected. The invention, unlike existing technology, involves not only software authentication but also hardware authentication. and enables verification of environmental characteristics. This method is more suitable for 6G networks. By providing a secure connection infrastructure, it minimizes the risk of unauthorized access. 35 2 Figures that will help understand the invention. Figure 1 shows a general representation of the system that is the subject of the invention. Explanation of Part References 1: RF Fingerprint Analysis Module 2: Channel State Information (CSI) Processing Unit 3: Timing Deviation and Doppler Analyzer 10 4: Security Decision-Making Mechanism 5: Dynamic Threshold Update Unit 6: Network Management and Alarm System Detailed Description of the Invention 15 The invention improves connection security between user equipment and base stations in 6G networks. It is a system that provides this through physical layer parameters. The system is RF Fingerprint Analysis. Module (1), Channel State Information (CSI) Processing Unit (2), Timing Deviation and Doppler Analyzer (3), Security Decision Mechanism (4), Dynamic Threshold Update Unit (5) and Network 20 It includes Management and Alarm System (6). RF Fingerprint Analysis Module (1) is manufactured to the tolerances in the RF chain of the base station. It analyzes the characteristic distortions caused by phase noise in the signal. By measuring carrier frequency shift and power amplifier linearity errors, each base station can be identified. It generates a unique RF fingerprint. This fingerprint serves as the hardware identifier of the base station. It is accepted and almost impossible to imitate. Channel Status Information (CSI) Processing Unit (2) is the unit that processes the information between user equipment and the base station. It analyzes the multipath propagation medium. This unit examines the path the signal takes, reflections, and attenuation. The CSI Processing Unit (2) continuously updates the channel matrix containing the effects of the channel. By calculating the temporal and frequency correlation, a 30-point analysis specific to the base station's location can be performed. It creates a channel profile. Timing Deviation and Doppler Analyzer (3) detects deviations in the transmission time of the signal and It measures frequency shifts caused by mobility. This module ensures the base station is at a fixed point. It checks whether it is in the correct position and the consistency of its distance from the user's equipment. Artificial signal delays, especially those caused by fake base stations, are particularly sensitive to this analyzer 35. It is detected by. 3 Security Decision Mechanism (4), RF Fingerprint Analysis Module (1), Channel Status Information (CSI) From the Processing Unit (2) and Timing Deviation and Doppler Analyzer (3) units This mechanism combines the received data with a predefined security threshold. It calculates a risk score by comparing RF fingerprint, channel profile and timing data. If it doesn't match the legitimate base station profile, the connection is rejected. 5 Dynamic Threshold Update Unit (5) automatically updates safety thresholds according to environmental conditions. It adjusts the channel accordingly. For example, in heavy rainfall or during high traffic situations. Since its characteristics may change, this unit can be normally used with machine learning algorithms. It updates the behavioral models. This minimizes the system's false alarm rate. Network Management and Alarm System (6), 10 detected by Security Decision Mechanism (4) It reports threats. When a fake base station attempt is detected, this system immediately alerts the network. It sends an alarm to the operator and switches user equipment in the affected area to safe mode. It passes. The system's operating scenario is as follows: User equipment connects to a base station. When you send the request, the RF Fingerprint Analysis Module (1) base station hardware 15 It measures its characteristics. Simultaneously, the Channel State Information (CSI) Processing Unit (2) and Timing Deviation and Doppler Analyzer (3) collects environmental and physical channel data. All collected data is processed by the Security Decision Mechanism (4) and a security score is given. is generated. If the score is within the safe range determined by the Dynamic Threshold Update Unit (5) Otherwise, the Network Management and Alarm System (6) will be activated, blocking the connection and security 20 reports the violation. 30 35

Claims

4 REQUESTS 1. In 6G networks, this occurs during the connection between user equipment and the base station. physical channel characteristics, signal timing deviation, channel state information, Doppler By analyzing its behavior and RF fingerprint, it can detect fake base stations, identity theft, or 5 It is a physical layer security authentication system that detects unauthorized access attempts. feature; - characteristics arising from manufacturing tolerances in the base station's RF chain. RF Fingerprint Analysis Module (1) that generates hardware identification by analyzing malfunctions. - by analyzing the multipath propagation medium between user equipment and base station 10 Channel State Information (CSI) Processing Unit (2) which forms the channel matrix, - deviations in signal transmission time and frequency shifts caused by mobility Timing Deviation and Doppler Analyzer (3), - RF Fingerprint Analysis Module (1), Channel Status Information (CSI) Processing Unit (2) and By combining the data from the Timing Deviation and Doppler Analyzer (3) units, 15 The Security Decision Mechanism (4) which calculates the risk score. - Dynamic Threshold, which automatically adjusts safety thresholds according to environmental conditions. Update Unit (5) and - Network Management that reports detected threats and generates alarms in case of security breaches. and Alarm System (6) 20 It includes.

2. According to Claim 1, the connection between user equipment and base station in 6G networks. physical channel characteristics, signal timing deviation, and channel status that occur during the process. By analyzing its information, Doppler behavior, and RF fingerprint, it can identify a fake base station or identity. Physical layer security authentication that detects imitation or unauthorized access attempts 25 It is a system whose feature is that it uses RF fingerprint, channel status and timing data. a security score is calculated and when this score is below a specified threshold value In this case, it includes a Security Decision Mechanism (4) that rejects the connection.

3. According to Claim 1, the connection between user equipment and base station in 6G networks. physical channel characteristics, signal timing deviation, channel state 30 that occur during this process By analyzing its information, Doppler behavior, and RF fingerprint, it can identify a fake base station or identity. Physical layer security authentication that detects spoofing or unauthorized access attempts. It is a system whose characteristic is that it analyzes changes in environmental conditions to determine normal behavior. updating its models and thereby dynamically adjusting security thresholds It includes a Dynamic Threshold Update Unit (5). 35

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