认知无线电网络基于信道特征的主用户仿真攻击检测技术研究

发布时间：2018-04-29 20:44

本文选题：认知无线电网络 + 主用户仿真攻击　；参考：《浙江大学》2017年硕士论文

【摘要】：认知无线电网络通过动态频谱接入机制,可以有效利用空闲频谱资源,缓解频谱资源紧张问题。动态频谱接入要求从用户在不干扰主用户网络工作的前提下伺机接入频谱,然而这种机制为认知无线电网络引入了新的安全问题。主用户仿真攻击(Primary User Emulation Attack,PUEA)是一类典型攻击,恶意用户通过模仿主用户特征使从用户错误判断当前频谱已被使用,从而失去接入空闲频谱的机会。PUEA问题干扰认知无线电网络的正常工作,寻找有效的PUEA防御策略是认知无线电网络安全的研究热点。基于此,论文研究基于信道特征的PUEA检测技术。针对主用户先验信息已知的PUEA检测问题,论文提出了一种基于信道多径时延差的PUEA检测方法。该方法采用多径信道的传播时延差作为检验统计量,并根据主用户先验信息与预设判决门限构建二元假设检验,实现PUEA的检测。同时,以虚警概率和漏警概率为性能指标,建模分析了所提出的PUEA检测方法的性能。另外,利用通用软件无线电外设搭建实验平台,在实际场景中验证所提出的PUEA检测方法的有效性。计算机仿真与实验测试结果表明,基于信道多径时延差的PUEA检测方法能在满足低虚警概率条件下,达到较高的检测概率。针对主用户先验信息未知与环境参数变化的PUEA检测问题,论文提出了一种基于增强学习的PUEA检测方法。首先构建基于单主用户与多主用户的认知无线电网络模型,并描述引入奖惩机制后频谱内各用户的工作流程。然后根据不同系统模型,分析从用户的奖惩收益情况,提出基于Q-Learning的PUEA检测方法。该方法采用信道多径时延差为状态参数,以判决门限为动作策略,优化目标为长时检测收益(从用户获得的奖罚反馈),寻找不同环境下的最优判决门限。仿真结果表明,基于Q-Learning的PUEA检测方法能根据网络环境参数变化实时调整判决门限,进而得到较好的检测性能,有效提升从用户的检测收益。
[Abstract]:Cognitive radio network can utilize the free spectrum resource effectively and alleviate the problem of spectrum resource shortage through dynamic spectrum access mechanism. Dynamic spectrum access requires slave users to access the spectrum without interfering with the work of the primary user network. However, this mechanism introduces a new security problem for cognitive radio networks. Primary User Emulation attack is a typical attack in which malicious users misjudge that the current spectrum has been used by imitating the characteristics of the primary user. Therefore, the problem of missing the opportunity to access the idle spectrum interferes with the normal work of cognitive radio networks. Finding effective PUEA defense strategies is a hot topic in the research of cognitive radio network security. Based on this, this paper studies the PUEA detection technology based on channel features. In order to solve the problem of PUEA detection based on prior information of primary user, a PUEA detection method based on channel multipath delay difference is proposed in this paper. In this method, the propagation delay difference of multipath channel is used as the test statistic, and the binary hypothesis test is constructed according to the priori information of the primary user and the preset decision threshold to realize the detection of PUEA. At the same time, using false alarm probability and false alarm probability as performance index, the performance of the proposed PUEA detection method is modeled and analyzed. In addition, the experiment platform is built with the general software radio peripheral, and the effectiveness of the proposed PUEA detection method is verified in the actual scene. Computer simulation and experimental results show that the PUEA detection method based on channel multipath delay difference can achieve high detection probability under the condition of low false alarm probability. Aiming at the problem of PUEA detection with unknown priori information and change of environmental parameters of primary users, a PUEA detection method based on reinforcement learning is proposed in this paper. Firstly, a cognitive radio network model based on single master user and multiple master user is constructed, and the workflow of each user in the spectrum after the introduction of reward and punishment mechanism is described. Then, according to different system models, this paper analyzes the reward and punishment income of users, and puts forward a PUEA detection method based on Q-Learning. In this method, the channel multipath delay difference is used as the state parameter, the decision threshold is used as the action strategy, and the objective is to detect the income in a long time. (the reward and penalty feedback from the user is obtained to find the optimal decision threshold in different environments. The simulation results show that the PUEA detection method based on Q-Learning can adjust the decision threshold in real time according to the change of network environment parameters, and then obtain better detection performance, and effectively enhance the detection income from users.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN925

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