Physical layer deception detection method based on deep reinforcement learning
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A technology of reinforcement learning and deception detection, applied in neural learning methods, biological neural network models, electrical components, etc., to achieve fast and accurate detection and dynamic continuous selection.
Pending Publication Date: 2022-08-02
SOUTHEAST UNIV
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However, most of the current research on spoofing attack detection is discrete threshold analysis, and there are few literature studies on the continuous control of detection thresholds. The determination of detection thresholds is particularly important for the performance of spoofing attack detection. How to learn continuous thresholds for dynamic environments still needs to be further studied.
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Embodiment 1
[0038] Example 1: see figure 1 , figure 2 , a physical layer deception detection method based on deep reinforcement learning, including the following steps:
[0039] Step 1. Establish a spoofing attack scenario, and the receiver extracts the physical layer channel state information between the sender and the receiver to represent the physical layer fingerprint feature;
[0040] Step 2. Build a binary hypothesis testing model;
[0041] Step 3, constructing the state value with the dynamic physical layer fingerprint feature, selecting and constructing the behavior value with the threshold value, using the Bayesian risk function as the instantaneous benefit function, and establishing the state-behavior-benefit triplet;
[0042] Step 4. Based on the deep deterministic policy gradient framework, a dynamic selection method of detection threshold is designed to detect physical layer spoofing attacks.
[0043] The spoofing attack scenario established in step 1 is that Alice and Bo...
Embodiment 2
[0067] Example 2: as figure 1 As shown, it is a schematic diagram of a spoofing attack scenario. The present invention proposes a physical layer spoofing detection method based on deep reinforcement learning. The method specifically includes the following steps:
[0068] Step 1. Establish a spoofing attack scenario. Alice and Bob are legitimate users, Alice is the transmitter, Bob is the receiver, and Eve is the spoofed user; it is assumed that the distance between the spoofed attacker and the legitimate transmitter and legitimate receiver is greater than half a distance. Wavelength, that is, the illegal channel is not related to the legal channel; the spoofing attack is an active attack, that is, the spoofing attacker and the legitimate transmitter do not transmit signals at the same time, and the attack probability of Eve pretending to be Alice to send a spoofing signal to Bob is y∈[0,Y MAX ), where the maximum attack probability Y MAX <1. The physical layer channel inform...
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Abstract
The invention provides a physical layer spoofing detection method based on deep reinforcement learning, and mainly solves the problems that a dynamic unknown wireless environment, a channel model or parameters are difficult to obtain and a fixed detection threshold is difficult to accurately select in the existing physical layer spoofing detection method. The method comprises the following implementation steps: 1) establishing a spoofing attack scene, and extracting physical layer channel information between a receiving party and a transmitting party by a receiving party to represent physical layer fingerprint characteristics; 2) establishing a binary hypothesis test model; 3) constructing a state value according to the dynamic physical layer fingerprint features, selecting and constructing a behavior value according to a threshold value, and establishing a state-behavior-benefit triple by taking a Bayesian risk function as an instantaneous benefit function; and 4) based on a depth deterministic strategy gradient framework, designing a detection threshold dynamic selection method, and detecting a physical layer spoofing attack. According to the method, dynamic continuous selection of the detection threshold can be realized, the method has adaptivity to a dynamic unknown environment, and the physical layer spoofing attack can be effectively detected.
Description
technical field [0001] The invention relates to a detection method, in particular to a physical layer deception detection method based on deep reinforcement learning, and belongs to the field of wireless communication physical layer security. Background technique [0002] With the large-scale deployment of 5G commercial use, the exploration and research of 6G is gradually being carried out. The IMT-2030 (6G) network technology group proposed to establish a physical layer security endogenous protection system of "active immunity and elastic autonomy" in the white paper "6G Network Architecture Vision and Key Technology Outlook" released on September 16, 2021. The deep integration of physical layer security and spoofing detection is of key significance for solving the problem that wireless networks are vulnerable to spoofing attacks and deepening the concept of endogenous security. [0003] The existing physical layer spoofing detection design methods mainly include tradition...
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