基于DNA动态编码和VMP-WFRFT的安全通信方法

doi:10.12382/bgxb.2022.0275

摘要/Abstract

摘要：

为提高物理层信息传输安全,提出一种基于DNA动态编码和变化多参数加权分数傅里叶变换(VMP-WFRFT)的安全通信方法。将DNA编码引入比特置乱,针对现有DNA编码规则少所存在的安全隐患,构造低复杂度四维混沌序列,利用混沌序列随机选择比特矩阵的DNA编码以及运算规则;为进一步掩盖信号调制样式,利用混沌序列动态调整MP-WFRFT参数,从而对调制信号进行VMP-WFRFT变换实现星座的混淆、扩散,同时增强了MP-WFRFT的参数抗扫描能力。仿真结果表明,该方法密钥敏感性良好、密钥空间大,能有效抵御穷举攻击,即使作为加密密钥的混沌初始值仅存在10^-15偏差,窃听者的误比特率仍始终保持在0.5左右。

关键词: 物理层安全, 混沌加密, DNA动态编码, MP-WFRFT

Abstract:

To improve the information transmission security of the physical layer, a secure communication method based on dynamic DNA coding and variable multi-parameter weighted fractional fourier transform (VMP-WFRFT) is proposed. DNA coding is introduced into bit scrambling. To address the hidden security problem of few existing DNA coding rules, a low complexity four-dimensional chaotic sequence is constructed, and the chaotic sequence is used to randomly select the DNA coding and operation rules of the bit matrix. In order to further mask the signal modulation pattern, the multi-parameter weighted-type fractional fourier transform (MP-WFRFT) parameters are dynamically adjusted by chaotic sequence, so that the modulated signal is transformed by VMP-WFRFT to realize the confusion and diffusion of the constellation, which also enhances the anti-scanning capability of the MP-WFRFT parameters. The simulation results show that the method has good key sensitivity and large key space, and can effectively resist exhaustive attacks. Even if the initial value of chaos as the encryption key has only 10^-15 deviations, the bit error rate of the eavesdropper is always maintained at about 0.5.

Key words: physical layer security, chaos encryption, dynamic DNA coding, multi-parameter weighted fractional Fourier transform

中图分类号:

TN918.91

王西康, 孟庆微, 许华, 齐子森, 张悦. 基于DNA动态编码和VMP-WFRFT的安全通信方法[J]. 兵工学报, 2023, 44(8): 2521-2532.

WANG Xikang, MENG Qingwei, XU Hua, QI Zisen, ZHANG Yue. Secure Communication Method Based on Dynamic DNA Coding and VMP-WFRFT[J]. Acta Armamentarii, 2023, 44(8): 2521-2532.

图/表 21

参考文献 29

[1]	MUCCHI L, JAYOUSI S, CAPUTO S, et al. Physical-layer security in 6G networks[J]. IEEE Open Journal of the Communications Society, 2021, 2: 1901-1914. doi: 10.1109/OJCOMS.2021.3103735 URL
[2]	王颖, 张晓忠, 曾娟, 等. 基于二级混沌映射的OFDM安全传输方案[J]. 通信学报, 2016, 37(7):132-139. doi: 10.11959/j.issn.1000-436x.2016141
	WANG Y, ZHANG X Z, ZENG J, et al. A secure OFDM transmission scheme based on two-level chaotic mapping[J]. Journal of Communication, 2016, 37(7):132-139. (in Chinese)
[3]	倪磊, 达新宇, 胡航, 等. 基于改进Logistic相位扰码的抗截获通信[J]. 华中科技大学学报, 2019, 47(6): 35-40.
	NI L, DA X Y, HU H, et al. Anti-interception communication based on improved logistic phase scrambling code[J]. Journal of Huazhong University of Science and Technology, 2019, 47(6): 35-40. (in Chinese)
[4]	HU Z Y, CHAN C K. A 7-D hyperchaotic system-based encryption scheme for secure fast-OFDM-PON[J]. Journal of Lightwave Technology, 2018, 36(16): 3373-3381. doi: 10.1109/JLT.2018.2841042 URL
[5]	SULTAN A, YANG X L, HAJOMER A, et al. Chaotic distribution of QAM symbols for secure OFDM signal transmission[J]. Optical Fiber Technology, 2019, 47: 61-65. doi: 10.1016/j.yofte.2018.11.022
[6]	牛莹, 张勋才. 基于变步长约瑟夫遍历和DNA动态编码的图像加密算法[J]. 电子与信息学报, 2020, 42(6): 1383-1391.
	NIU Y, ZHANG X C. Image encryption algorithm based on variable-step Joseph traversal and DNA dynamic coding[J]. Journal of Electronics and Information, 2020, 42(6): 1383-1391. (in Chinese)
[7]	张勋才, 刘奕杉, 崔光照. 基于DNA编码和超混沌系统的图像加密算法[J]. 计算机应用研究, 2019, 36(4): 1139-1143.
	ZHANG X C, LIU Y S, CUI G Z. An image encryption algorithm based on DNA coding and hyperchaotic systems[J]. Computer Application Research, 2019, 36(4): 1139-1143. (in Chinese)
[8]	李锦青, 刘泽飞, 满振龙. 基于生成对抗网络的密钥生成方法及其在微光图像加密中的应用[J]. 兵工学报, 2022, 43(2): 337-344.
	LI J Q, LIU Z F, MAN Z L. Key generation method based on generative adversarial network and its application in low-light-level image encryption[J]. Acta Armamentarii, 2022, 43(2): 337-344. (in Chinese) doi: 10.3969/j.issn.1000-1093.2022.02.011
[9]	梁源, 达新宇. 基于多参数加权分数阶傅里叶变换的星座预编码系统研究与实现[J]. 电子与信息学报, 2018, 40(4): 825-831.
	LIANG Y, DA X Y. Research and implementation of a constellation precoding system based on multi-parameter weighted fractional-order Fourier transform[J]. Journal of Electronics and Information, 2018, 40(4): 825-831. (in Chinese)
[10]	ZHANG N, FANG X J, WANG Y, et al. Physical-layer authentication for internet of things via WFRFT-based Gaussian tag embedding[J]. IEEE Internet of Things, 2020, 7(9): 9001-9010.
[11]	桑之昂, 刘亚南, 刘子威, 等. 基于WFRFT的卫星信号掩盖方法研究[J]. 电子与信息学报, 2022, 44(1): 339-345.
	SANG Z A, LIU Y N, LIU Z W, et al. Research on satellite signal masking method based on WFRFT[J]. Journal of Electronics and Information, 2022, 44(1): 339-345. (in Chinese)
[12]	张笑宇, 冯永新. 一种基于时分数据调制的加权分数阶傅里叶变换通信方法[J]. 兵工学报, 2020, 41(7): 1360-1367. doi: 10.3969/j.issn.1000-1093.2020.07.013
	ZHANG X Y, FENG Y X. A communication method based on time division modulation weighted fractional Fourier transform[J]. Acta Armamentarii, 2020, 41(7): 1360-1367. (in Chinese) doi: 10.3969/j.issn.1000-1093.2020.07.013
[13]	徐瑞阳, 达新宇, 梁源, 等. 基于跳频的改进加权分数阶傅里叶变换[J]. 华中科技大学学报(自然科学版), 2019, 47(2): 30-35.
	XU R Y, DA X Y, LIANG Y, et al. Improved weighted fraction Fourier transform based on frequency hopping[J]. Journal of Huazhong University of Science & Technology (Nature Science Edition), 2019, 47(2): 30-35. (in Chinese)
[14]	杨振鑫. WFRFT信号参数识别方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2020.
	YANG Z X. Studies on the methods of parameters identification of WFRFT signal[D]. Harbin: Harbin Institute of Technology, 2020. (in Chinese)
[15]	LIU F. A cross-hierarchical scanning method based SP-4-WFRFT for digital communication signals[J]. Mathematical Problems in Engineering, 2018, 2018: 6580146.
[16]	张笑宇, 宋碧雪, 王洋, 等. 基于循环相关的加权分数阶傅里叶变换信号旋转因子估计方法[J]. 兵工学报, 2022, 43(7):1646-1654. doi: 10.12382/bgxb.2021.0412
	ZHANG X Y, SONG B X, WANG Y, et al. An estimation method for rotation factor of weighted fractional Fourier transform signals based on cyclic correlation[J]. Acta Armamentarii, 2022, 43(7):1646-1654. (in Chinese)
[17]	MUHAMMAD S, WAQAR A, HIRA N, et al. An image encryption scheme based on DNA computing and multiple chaotic systems[J]. IEEE Access, 2020, 8: 25650-25663. doi: 10.1109/Access.6287639 URL
[18]	WU T W, ZHANG C F, HUANG H, et al. Security improvement for OFDM-PON via DNA extension code and chaotic systems[J]. IEEE Access, 2020, 8: 75119-75126. doi: 10.1109/Access.6287639 URL
[19]	CHEN G R, UETA T. Yet another chaotic attractor[J]. International Journal of Bifurcation and Chaos, 1999, 9(7): 1465-1466. doi: 10.1142/S0218127499001024 URL
[20]	SUN K H, LIU X, ZHU C X, et al. Hyperchaos and hyperchaos control of the sinusoidally forced simplified lorenz system[J]. Nonlinear Dynamics, 2012, 69(3): 1383-1391. doi: 10.1007/s11071-012-0354-x URL
[21]	HU Z Y, CHAN C K. A 7-D hyperchaotic system-based encryption scheme for secure fast-OFDM-PON[J]. Journal of Lightwave Technology, 2018, 36(16): 3373-3381. doi: 10.1109/JLT.2018.2841042 URL
[22]	ENAYATIFAR R, ABDULLAH A H, ISNIN I F, et al. Image encryption using a synchronous permutation-diffusiontechnique[J]. Optics & Lasers in Engineering, 2017, 90: 146-154.
[23]	NIYAT A Y, MOATTAR M H, TORSHIZ M N. Color image encryption based on hybrid hyper-chaotic system and cellular automata[J]. Optics & Lasers in Engineering, 2017, 90: 225-237.
[24]	LI C H, LUO G C, QIN K, et al. An image encryption scheme based on chaotic tent map[J]. Nonlinear Dynamics, 2017, 87(1): 127-133. doi: 10.1007/s11071-016-3030-8 URL
[25]	BELAZI A, EL-LATIF A A, BELGHITH S. A novel image encryption scheme based on substitution-permutation network and chaos[J]. Signal Processing, 2016, 128: 155-170. doi: 10.1016/j.sigpro.2016.03.021 URL
[26]	HUA Z Y, ZHOU Y C, PUN C M, et al. 2D Sine Logistic modulation map for image encryption[J]. Information Sciences, 2015, 297: 80-94. doi: 10.1016/j.ins.2014.11.018 URL
[27]	WANG X Y, LIU L T, ZHANG Y Q. A novel chaotic block image encryption algorithm based on dynamic random growth technique[J]. Optics and Lasers in Engineering, 2015, 66: 10-18. doi: 10.1016/j.optlaseng.2014.08.005 URL
[28]	肖成龙, 孙颖, 林邦姜, 等. 基于神经网络与复合离散混沌系统的双重加密方法[J]. 电子与信息学报, 2020, 42(3): 687-694.
	XIAO C L, SUN Y, LIN B J, et al. Double encryption method based on neural network and composite discrete chaotic system[J]. Journal of Electronics & Information Technology, 2020, 42(3): 687-694. (in Chinese)
[29]	KHAN M, MASOOD F. A novel chaotic image encryption technique based on multiple discrete dynamical maps[J]. Multimedia Tools and Applications, 2019, 78(18): 26203-26222. doi: 10.1007/s11042-019-07818-4

混沌系统	加法	乘法	正弦函数	密钥空间
本文系统	11	16	2	10^81.2
Chen等^[19]attractor系统	41	38	0	10⁴⁵
4-D chaos系统^[20]	41	54	8	10^70.7
7-D chaos系统^[21]	221	118	4	>10¹⁰⁵

混沌系统	加法	乘法	正弦函数	密钥空间
本文系统	11	16	2	10^81.2
Chen等^[19]attractor系统	41	38	0	10⁴⁵
4-D chaos系统^[20]	41	54	8	10^70.7
7-D chaos系统^[21]	221	118	4	>10¹⁰⁵

图像	图片尺寸/像素	信息熵
图像	图片尺寸/像素	原始图像	文献[22]算法	文献[23]算法	文献[24]算法	本文算法
	256×256	3.8212	7.9958	7.9972	7.9909	7.9975
	512×512	3.3097	7.9983	7.999	7.9921	7.9992
	256×256	3.2677	7.9974	7.997	7.9912	7.9973
	512×512	3.1958	7.9991	7.9991	7.9925	7.9992
	256×256	3.5024	7.9939	7.9971	7.9915	7.9973
	512×512	3.2639	7.9972	7.9993	7.9923	7.9993
	256×256	3.6347	7.9975	7.9974	7.9913	7.9973
	512×512	3.1435	7.9994	7.9995	7.9924	7.9994
	256×256	3.2921	7.9941	7.9973	7.9907	7.9971
	512×512	3.2827	7.9988	7.9992	7.9924	7.9994
	256×256	3.5501	7.9938	7.997	7.9912	7.9972
	512×512	3.2925	7.9981	7.999	7.9922	7.9993

图像	图片尺寸/像素	信息熵
图像	图片尺寸/像素	原始图像	文献[22]算法	文献[23]算法	文献[24]算法	本文算法
	256×256	3.8212	7.9958	7.9972	7.9909	7.9975
	512×512	3.3097	7.9983	7.999	7.9921	7.9992
	256×256	3.2677	7.9974	7.997	7.9912	7.9973
	512×512	3.1958	7.9991	7.9991	7.9925	7.9992
	256×256	3.5024	7.9939	7.9971	7.9915	7.9973
	512×512	3.2639	7.9972	7.9993	7.9923	7.9993
	256×256	3.6347	7.9975	7.9974	7.9913	7.9973
	512×512	3.1435	7.9994	7.9995	7.9924	7.9994
	256×256	3.2921	7.9941	7.9973	7.9907	7.9971
	512×512	3.2827	7.9988	7.9992	7.9924	7.9994
	256×256	3.5501	7.9938	7.997	7.9912	7.9972
	512×512	3.2925	7.9981	7.999	7.9922	7.9993

图像名称	水平方向	垂直方向	斜线方向
Lena	0.0023	0.0003	0.0014
Lena^[25]	-0.0048	-0.0112	-0.0045
Cameraman	-0.0083	0.0086	0.0113
Cameraman^[25]	-0.0095	-0.0170	-0.0170
Airplane	-0.0133	0.0008	0.0028
Airplane^[26]	0.0180	0.0061	-0.0079
Peppers	0.0128	-0.0021	-0.0056
Peppers^[27]	0.0194	0.0194	0.0123