Behavioral Steganography in Social Networks

Year 2022, Volume: 11 Issue: 4, 135 - 141, 28.12.2022

Muharrem Tuncay Gençoğlu

https://doi.org/10.46810/tdfd.1208075

Abstract

Recently, using human behavior to hide the existence of information has been at the center of steganography research. In this study, a behavioral steganography algorithm using CMI (Coded Signal Inversion) coding is proposed to minimize the high bit error rate that occurs when transmitting a large number of continuous and identical confidential information in the knapsack algorithm, which is used to improve information transmission efficiency and flexibility of transmission mode in social networks. In the proposed algorithm; Data redundancy is reduced by reducing the number of mutual friends of the sender and each receiver. Then, the proposed algorithm was applied and the results were analyzed. Experimental analysis shows that this scheme improves the practical value of behavioral steganography in social networks and has high security.

Keywords

behavior steganography, hide the existence of the information, knapsack algorithm, social network, steganography

References

• Çıtlak O, Dörtler M. and Doğru, İ. A. A survey on detecting spam accounts on Twitter network. Soc.Netw. Anal. Min. 2019, 9:35.
• Dutta H, Das R K, Nandi S, An overview of digital audio steganography. IETE Technical Review, 2020, 37(6): 632 - 650.
• Evsutin O, Melman S, Meshcheryakov R, V. Digital steganography and watermarking for digital images: a review of current research directions. IEEE Access, 2020, 8: 166589 - 166611.
• Han X, Li G. Dynamic cat transformation and chaotic mapping image encryption algorithm. Computer Engineering and Design, 2020, 41(08): 2381 - 2387.
• Hu F. A probabilistic solution discovery algorithm for solving 0-1 knapsack problem. International Journal of Parallel, Emergent, and Distributed Systems, 2018, 33(6): 618 - 626.
• Hu Y, Wang Z, Zhang X. Steganography in social networks based on behavioral correlation. IETE Technical Review, 2020, 38(1): 93 - 99.
• Kantartopoulos P, Pitropakis N, Mylonas, A. Exploring adversarial attacks and defenses for fake Twitter account detection. Technologies, 2020, 8 (4): 64.
• Li S, Zhang X. Towards construction based data hiding: from secrets to fingerprint images. IEEE Transactions on Image Processing, 2019, 28(3): 1482 - 1497.
• Li S, H, Wang Z, Lost in the digital wild: hiding information in digital activities. Proceedings of the 2nd International Workshop on Multimedia Privacy and Security. Toronto, Canada: Associate for Computer Mair Coolinghinery, 2018: 27 - 37.
• Mesut A, Veri Sikiştirmada Yeni Yöntemler (Doktora Tezi), Trakya Üniversitesi Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Anabilim Dalı, 2006. http://dspace.trakya.edu.tr/xmlui/bitstream/handle/trakya/590/0042520.pdf?sequence=1, Erişim 15.04.2022
• Pantic N, Husain M. Covert botnet command and control using Twitter. Proceedings of the 31st Annual Computer Security Applications Conference. Los Angeles: Associate for Computer Mair coolinghinery, 2015: 171 - 180.
• Subramanian B, Yesudhas H, Enoch G. Channel-based encrypted binary arithmetic coding in a wireless sensor network. Ingénierie des Systèmes d'Information, 2020, 25(2): 199 - 206.
• Wang Z, Zhang X, Yin Z. Joint cover-selection and payload-allocation by stenographic distortion optimization. IEEE Signal Process Lett, 2018, 25(10): 1530 -1534.
• Yan W, Min W, Jia L, Xiang X. Algorithmic decision analysis of 0-1 knapsack problem. Computer Knowledge and Technology, 2020, 16(04): 259 - 264.
• Zhang W, Qin Z, Feng Z, Liu J, Liu W, Tang X. Big data analysis for detection of web brute-force attack. Journal of Shenzhen University Science and Engineering), 2020, 37(S1): 44 - 49.
• Zhang X. Behavior Steganography in Social Network. Taiwan, China: Springer International Publishing, 2017: 21 - 23.
• Zhao X, Cheng Y, Zuo L, Fang Y. Design of CMI CODEC based on FPGA. Modern Information Technology, 2020, 4(19): 35 - 37.
• http://www.interfacebus.com/CMI_Encoding_Definition.html, Erişim: 15.04.2022