Peer-to-Peer Networking and Applications

, Volume 12, Issue 6, pp 1799–1809 | Cite as

A code protection method against function call analysis in P2P network

  • Fei Xiang
  • Daofu GongEmail author
  • Jie Li
  • Fenlin Liu
Part of the following topical collections:
  1. Special Issue on Networked Cyber-Physical Systems


The P2P network has the characteristics of opening and sharing, and a large number of managing and controlling software are deployed on the distributed network nodes. Hence, it is a significant problem to protect software on these untrusted nodes from being maliciously reversed and tampered, and eventually guarantee the P2P network security. Function calls are often the important targets of reverse analysis, which can reveal the software structure and functionality and contribute to malicious attacks. Attackers can identify function calls and execution paths through static code analysis, and can also obtain function call sequences and determine function call relations through dynamic stack backtracking analysis. In terms of these problems, this paper proposes a code protection method against function call analysis. In the static aspect, the techniques such as function address mapping and instruction overlap are employed to hide the function execution paths. In the dynamic aspect, the techniques such as stack frame migration are used to protect the function call sequences and relations from stack backtracking. The method is evaluated in terms of validity, space overhead and time overhead respectively. The experimental results indicate that the method can effectively resist some specific static and dynamic reverse analysis of function calls, and has good space and time overhead performances.


Software protection Code obfuscation Function calls Stack backtrack 



This research was supported by the National Natural Science Foundation of China (No. 61272489, 61302159, 61379151, 61602508, 61772549).


  1. 1.
    Balachandran V, Keong NW, Emmanuel S (2014) Function level control flow obfuscation for software security. In: 2014 8th international conference on complex, intelligent and software intensive systems (CISIS), pp 133–140. IEEEGoogle Scholar
  2. 2.
    Barak B, Goldreich O, Impagliazzo R, Rudich S, Sahai A, Vadhan S, Yang K (2001) On the (im) possibility of obfuscating programs. In: Annual international cryptology conference, pp 1–18. SpringerGoogle Scholar
  3. 3.
    Ceccato M, Di Penta M, Falcarin P, Ricca F, Torchiano M, Tonella P (2014) A family of experiments to assess the effectiveness and efficiency of source code obfuscation techniques. Empir Softw Eng 19 (4):1040–1074Google Scholar
  4. 4.
    Choudhary U, Yadav M (2015) Review on reverse engineering techniques of software engineering. International Journal of Computer Applications 119(14):7–10CrossRefGoogle Scholar
  5. 5.
    Collberg C, Thomborson C, Low D (1997) A taxonomy of obfuscating transformations. Tech. rep., Department of Computer Science, The University of Auckland, New ZealandGoogle Scholar
  6. 6.
    Collberg CS, Thomborson C, Low DWK (2003) Obfuscation techniques for enhancing software security. US Patent 6:668,325Google Scholar
  7. 7.
    Gautam P, Saini H (2017) A novel software protection approach for code obfuscation to enhance software security. International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 8(1):34–47CrossRefGoogle Scholar
  8. 8.
    Jang Y, Kim J, Lee W (2018) Development and application of internet of things educational tool based on peer to peer network. Peer-to-Peer Networking and Applications 11(6):1217–1229CrossRefGoogle Scholar
  9. 9.
    Klimek I, Keltika M, Jakab F (2011) Reverse engineering as an education tool in computer science. In: 2011 9th international conference on emerging eLearning technologies and applications (ICETA), pp 123–126. IEEEGoogle Scholar
  10. 10.
    Kulkarni A, Metta R (2014) A new code obfuscation scheme for software protection. In: 2014 IEEE 8th international symposium on service oriented system engineering (SOSE), pp 409–414. IEEEGoogle Scholar
  11. 11.
    LeDoux C, Sharkey M, Primeaux B, Miles C (2012) Instruction embedding for improved obfuscation. In: Proceedings of the 50th annual southeast regional conference, pp 130–135. ACMGoogle Scholar
  12. 12.
    Madou M, Anckaert B, Moseley P, Debray S, De Sutter B, De Bosschere K (2005) Software protection through dynamic code mutation. In: International Workshop on information security applications, pp 194–206. SpringerGoogle Scholar
  13. 13.
    Majumdar A, Thomborson C (2006) Manufacturing opaque predicates in distributed systems for code obfuscation. In: Proceedings of the 29th Australasian computer science conference-volume 48, pp 187–196. Australian Computer Society, IncGoogle Scholar
  14. 14.
    Mavrogiannopoulos N, Kisserli N, Preneel B (2011) A taxonomy of self-modifying code for obfuscation. Comput Secur 30(8):679–691CrossRefGoogle Scholar
  15. 15.
    Ogiso T, Sakabe Y, Soshi M, Miyaji A (2003) Software obfuscation on a theoretical basis and its implementation. IEICE Trans Fundam Electron Commun Comput Sci 86(1):176–186Google Scholar
  16. 16.
    Pavlovic D (2011) Gaming security by obscurity. In: Proceedings of the 2011 new security paradigms workshop, pp 125–140. ACMGoogle Scholar
  17. 17.
    Schrittwieser S, Katzenbeisser S (2011) Code obfuscation against static and dynamic reverse engineering. In: International workshop on information hiding, pp 270–284. SpringerGoogle Scholar
  18. 18.
    Sebastian SA, Malgaonkar S, Shah P, Kapoor M, Parekhji T (2016) A study & review on code obfuscation. In: World conference on futuristic trends in research and innovation for social welfare (Startup Conclave), pp 1–6. IEEEGoogle Scholar
  19. 19.
    Shi Z, Zhou C, Gu Y, Goodman NA, Qu F (2017) Source estimation using coprime array: A sparse reconstruction perspective. IEEE Sensors J 17(3):755–765CrossRefGoogle Scholar
  20. 20.
    Suenaga M (2009) A museum of api obfuscation on win32. In: Proceedings of 12th association of anti-virus asia researchers international conference, AVAR, vol 2009Google Scholar
  21. 21.
    Wang C, Hill J, Knight J, Davidson J (2000) Software tamper resistance: Obstructing static analysis of programs. Tech. rep., Technical Report CS-2000-12, University of Virginia, 12 2000Google Scholar
  22. 22.
    Xie X, Liu F, Lu B (2014) A data obfuscation based on state transition graph of mealy automata. In: International conference on intelligent computing, pp 520–531. SpringerGoogle Scholar
  23. 23.
    Xie X, Liu F, Lu B, Xiang F (2016) An iteration obfuscation based on instruction fragment diversification and control flow randomization. International Journal of Computer Theory and Engineering 8(4):303CrossRefGoogle Scholar
  24. 24.
    Yang G, He S, Shi Z (2017) Leveraging crowdsourcing for efficient malicious users detection in large-scale social networks. IEEE Internet Things J 4(2):330–339CrossRefGoogle Scholar
  25. 25.
    Yang G, He S, Shi Z, Chen J (2017) Promoting cooperation by the social incentive mechanism in mobile crowdsensing. IEEE Commun Mag 55(3):86–92CrossRefGoogle Scholar
  26. 26.
    You I, Yim K (2010) Malware obfuscation techniques: A brief survey. In: 2010 International conference on broadband, wireless computing, communication and applications (BWCCA), pp 297–300. IEEEGoogle Scholar
  27. 27.
    Zhou C, Gu Y, He S, Shi Z (2018) A robust and efficient algorithm for coprime array adaptive beamforming. IEEE Trans Veh Technol 67(2):1099–1112CrossRefGoogle Scholar
  28. 28.
    Zhou Y, Tang W, Zhang D, Lan X, Zhang Y (2017) A case for software-defined code scheduling based on transparent computing. Peer-to-Peer Networking and Applications, pp 1–11Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State key Laboratory of Mathematical Engineering and Advanced ComputingZhengzhouChina

Personalised recommendations