| 摘要: |
| 随着物联网技术的发展, 组播通信的需求日益增大。异或加密作为最简单高效的加密方法之一, 在信息安全方面有着广泛的应用。本文针对组播通信安全需求, 设计了一种基于异或自反性和射频指纹的组密钥生成方法。为解决多个终端在密钥生成过程中的传输资源选择冲突问题, 提出基于扩频和公私钥密码体系的用户标识方法。先利用射频指纹对用户认证, 并在组播用户间形成密钥随机源; 然后, 利用异或的自反特性实现分布式密钥生成。将射频指纹与公私钥密码体系结合, 不仅为射频指纹的识别结果提供了参考, 还为组播通信下密钥协商时的通信资源选择提供了方法。为评估射频指纹识别的影响, 提出并实验验证了一种基于时频分析与深度学习的射频指纹识别算法。最后, 分析了所提方法的密钥生成率、资源选择冲突和密钥生成效率,展示了所提方法的可行性和有效性。分析发现所提方法相比于传统方法, 分布式的密钥源使得密钥生成效率随着节点数的增大而提高。对组密钥被攻破概率的窃听模型仿真结果表明, 在生成同样长度的密钥时, 与遍历搜索密钥空间比较, 基于窃听者遍历搜索设备射频指纹特性的条件, 破解所提方法组密钥的复杂度要高出一至四个数量级, 验证了本文方法的安全性。 |
| 关键词: 组密钥生成 扩频技术 射频指纹 异或的自反性 |
| DOI：10.19363/J.cnki.cn10-1380/tn.2024.05.03 |
| Received:June 06, 2022Revised:July 18, 2022 |
| 基金项目:本课题得到中央高校基本科研业务费专项资金(No. ZYGX2020ZB042)资助。 |
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| Group Key Generation Method Based on XOR Reflexivity and Radio Frequency Fingerprinting |
| KAI Genshen,MA Juntao,WU Gang,HU Su |
| School of Communication & Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China |
| Abstract: |
| With the development of Internet of Things (IoT), there is an increasing demand for multicast communication. As one of the simplest and most efficient encryption methods, XOR encryption has a wide range of applications in information security. Aiming at the security requirements of multicast communication, this paper designs a group key generation method based on XOR reflexivity and radio frequency fingerprinting (RFF). In order to solve the conflict of transmission resource selection of multiple terminals in the process of key generation, this paper proposes a terminal identification method based on spread spectrum technology and public-private key cryptosystem. RFF are used to authenticate terminals, and a random source of keys is formed among multicast users. The purpose of distributed key generation is achieved by using the XOR reflexivity. By combining the RFF with the public-private key cryptosystem, it not only provides a reference for the identification results of the RFF, but also provides a method for the selection of communication resources during key negotiation under multicast communication. To evaluate the impact of RFF, a RFF recognition algorithm based on time-frequency analysis and deep learning is proposed and experimentally verified. Finally, the key generation rate, resource selection conflict and key generation efficiency of the proposed method are analyzed to illustrate the feasibility and effectiveness of the proposed method. The analysis reveals that the proposed method has a distributed key source compared to the traditional method, which makes the key generation efficiency increase as the number of nodes increases. The simulation results of the eavesdropping model of the probability of group key being breached show that, when generating a key of the same length, compared with traversing the search key space, based on the condition of the eavesdropper traversing the search device's RFF, the complexity of cracking the group key is one to four orders of magnitude higher, which verifies the security of the method in this paper. |
| Key words: group key generation spread spectrum technology radio frequency fingerprinting XOR reflexivity |
