JIANG Jiajia1, LU Yin1, XU Junyu1, DUAN Fajie1, WANG Xianquan1, LIU Wei2, FU Xiao1
(1. State Key Lab of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; 2. Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield UK)
Abstract: When radio communication is interfered with or radio silence is required, the command and intelligence cannot be transmitted among the armed squads in the forest. So there is a great need for a communication method that can replace radio communication to transmit some important information. A novel acoustic covert communication method is proposed. This method encodes the communication information by imitating animals’ calls. Taking silvereye’s call as an example, silvereye’s syllables and the time intervals between syllables are used to encode the communication information. A syllable grouping method based on time-frequency characteristics is designed to improve encoding efficiency and reduce the amount of decoding computation. Meanwhile, various strategies are used to make the encoded communication pulse sequence as similar as possible to the true silvereye’s calls to improve the covert performance. The feasibility experiments are carried out in a forest with moderate noise to demonstrate the performance of the proposed covert communication method. The power of the acoustic source is fixed at 30 watts and the communication distance varies from 20 m to 60 m. The bit-error-rate (BER) of two communication modes are 1.98% and 1.39% at 60 m, respectively. Finally, the model of communication method is introduced, and the problems that may be encountered in practical applications, as well as solutions and future development directions are analyzed. The feasibility and covert performance of the proposed method are verified in the jungle environment, and the covert information transmission can be realized when radio communication is infeasible.
Key words: imitating brid calls; acoustic covert communication; silvereye’s call; time-frequency characteristics; radio communication
References
[1]SAARNISAARI H, BRYSY T. Future military mobile radio communication systems from electronic warfare perspective//2017 International Conference on Military Communications and Information Systems (ICMCIS), Jun.26, 2017, Oulu, Finland, NewYork: IEEE, 2017: 16981051.
[2]JAITLY S, MALHOTRA H, BHUSHAN B. Security vulnerabilities and countermeasures against jamming attacks in wireless sensor networks: A survey//2017 International Conference on Computer, Communications and Electronics (Comptelix), Jul. 1-2, 2017, Jaipur, India, New York: IEEE, 2017: 559-564.
[3]KADHIM A, SADKHAN S. Security threats in wireless network communication-status, challenges, and future trends//2021 International Conference on Advanced Computer Applications (ACA), Jul. 25-26, Maysan, Iraq, New York: IEEE, 2021: 176-181.
[4]QIAN Y, FENG Y, HSIAO-HWA C. Security in wireless communication networks. New York: John Wiley & Sons, 2021.
[5]HANSPACH M, GOETZ M. On covert acoustical mesh networks in air. Journal of Communications, 2013, 8(11): 758-767.
[6]CARRARA B, ADAMS C. On acoustic covert channels between air-gapped systems//International Symposium on Foundations and Practice of Security, Nov. 3-5, 2014, Montreal, QC, Canada, Cham: Springer, 2014: 3-16.
[7]DESHOTELS L. Inaudible sound as a covert channel in mobile devices//8th USENIX Workshop on Offensive Technologies (WOOT 14), Aug. 19, 2014, San Diego, America, New York: Usenix, 2014.
[8]MARSZAL J, SALAMON R. Distance measurement errors in silent FM-CW sonar with matched filtering. Metrology and Measurement Systems, 2012, 19: 321-332.
[9]ABD M H M, AMINIFAR S. A demodulator selection model for received FSK and ASK signals. Neuro Quantology, 2022, 20(10): 2181-2186.
[10]ALNAJJAR K, SARA G, SAM A. Automatic modulation classification in deep learning//2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA). Dec. 27-29, 2022, Cairo, Egypt, New York: IEEE, 2022: 22544346.
[11]QIU Z R, ZHANG Q, LI H Z, et al. Design of inductive coupling channel analysis system based on LabVIEW. Journal of Measurement Science and Instrumentation, 2018, 9(4): 360-366.
[12]ZHANG X M, ZHANG S X, LI Y. Classification method for communication modulation signal identification based on multiple feature extraction and Cubic SVM//2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE), Sep.23-25, 2022, Dalian, China, New York: IEEE, 2022: 22214149.
[13]O’SHEA T J, ROY T, CLANCY T C. Over-the-air deep learning based radio signal classification. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 168-179.
[14]CUI W, WU S, TIAN J, et al. Efficient weak manoeuvring target detection method for DSSS signal. Electronics Letters, 2014, 50(23): 1740-1741.
[15]BAKER M C. Silvereyes (Zosterops lateralis) song differentiation in an island-mainland comparison: analyses of a complex cultural trait. The Wilson Journal of Ornithology, 2012, 124(3): 454-466.
[16]SLATER P J. The relationship between individual variation in song and ecology in the Capricorn silvereye. 1993, 93(3): 145-155.
[17]BALABAN P, SALZ J. Dual diversity combining and equalization in digital cellular mobile radio. IEEE Transactions on vehicular technology, 1991, 40(2): 342-354.
[18]ANNAVAJJALA R, MILSTEIN L B. Performance analysis of linear diversity-combining schemes on Rayleigh fading channels with binary signaling and Gaussian weighting errors. IEEE Transactions on Wireless Communications, 2005, 4(5): 2267-2278.
[19]STAHL V, FISCHER A, BIPPUS R. Quantile based noise estimation for spectral subtraction and Wiener filtering//2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 00CH37100), Jun.5-9, 2000, Istanbul, Turkey, New York: IEEE, 2000: 6754861 .
[20]JALIL M, BUTT F A, MALIK A. Short-time energy, magnitude, zero crossing rate and autocorrelation measurement for discriminating voiced and unvoiced segments of speech signals//2013 The International Conference On Technological Advances In Electrical, Electronics And Computer Engineering (TAEECE), May 9-11, 2013, Konya, Turkey, New York: IEEE, 2013: 208-212.
一种基于仿鸟叫声的隐蔽通信方法
蒋佳佳1, 陆茵1, 徐俊宇1, 段发阶1, 王宪全1, 刘伟2, 傅骁1
(1. 天津大学 精密测量技术与仪器国家重点实验室, 天津 300072; 2. 谢菲尔德大学 电子与电气工程系, 英国 谢菲尔德)
摘要:当无线电通信受到干扰或要求无线电静默时, 在森林作战的武装队伍之间将无法进行命令和情报传递。 因此, 亟需一种可以代替无线电通信的通信方式用于传输重要信息。 针对这一需求, 提出了一种新型的隐蔽声通信方法, 该方法通过模仿动物的叫声来编码通信信息, 并以绣眼鸟的叫声为例, 利用绣眼鸟的叫声音节和音节之间的时间间隔来编码通信信息。 为了减少译码计算量并提高编码效率, 设计了一种基于时频特征的音节分组方法。 同时, 采用多种策略使得编码后的通信脉冲序列与真实铜蓝鹟叫声尽可能相似, 以提高隐蔽性能, 并通过实验验证在距离不超过60 m时, 通信误码率不超过2%。 最后, 介绍了该通信方法的模型, 并分析了在实际应用中可能遇到的问题, 以及解决方案和未来的发展方向。 研究结果表明, 本方法可实现无线电不可用情况下的信息隐蔽传递。
关键词:仿鸟叫声; 声学隐蔽通信; 绣眼鸟叫声; 时频特性; 无线电通信
引用格式:JIANG Jiajia, LU Yin, XU Junyu, et al. A covert communication method based on imitating bird calls. Journal of Measurement Science and Instrumentation, 2023, 14(4): 387-397. DOI: 10.3969/j.issn.1674-8042.2023.04.002
[full text view]