Análisis comparativo de técnicas de protección de software para prevenir el acceso no autorizado y la distribución ilegal Comparative analysis of software protection techniques to prevent unauthorized access and illegal distribution
Barra lateral del artículo
Contenido principal del artículo
Resumen
Proteger el software hoy en día no fue solo una cuestión técnica, es casi una carrera constante contra nuevas formas de ataque. Bajo esa idea, este estudio revisó 29 trabajos publicados entre 2020 y 2025, seleccionados cuidadosamente mediante Parsifal, con el propósito de entender qué soluciones se están aplicando realmente para enfrentar estos desafíos. Las investigaciones analizadas abordaron técnicas como la ofuscación de código, los mecanismos de licenciamiento y activación, el watermarking y el fingerprinting. Los resultados evidencian una tendencia hacia el uso de estrategias híbridas y complementarias, las cuales ofrecen un mejor equilibrio entre seguridad, rendimiento y compatibilidad frente a soluciones aisladas. Asimismo, se identificaron limitaciones relacionadas con la falta de estandarización de métricas de evaluación y la integración entre técnicas. Este trabajo aportó una visión estructurada al estado del arte y planteó orientaciones para futuras investigaciones en entornos digitales
Descargas
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Citas
Ahire, P., & Abraham, J. (2022). Secure cloud model for intellectual privacy protection of arithmetic expressions in source codes using data obfuscation techniques. En Theoretical Computer Science (Vol. 922, pp. 131-149). https://doi.org/10.1016/j.tcs.2022.04.018
Akram, J., Vasan, D., & Luo, P. (2022). Obfuscated code is identifiable by a token-based code clone detection technique. En International Journal of Information and Computer Security (Vol. 19, Números 3-4, pp. 254-273). https://doi.org/10.1504/ijics.2022.127132
Al-Hakimi, A. M. H., Sultan, A. B. M., Abdul Ghani, A. A., Ali, N. M., & Admodisastro, N. I. (2020). Hybrid Obfuscation Technique to Protect Source Code From Prohibited Software Reverse Engineering. IEEE Access, 8, 187326-187342. https://doi.org/10.1109/ACCESS.2020.3028428
Alrabaee, S., Debbabi, M., & Wang, L. (2022). A Survey of Binary Code Fingerprinting Approaches: Taxonomy, Methodologies, and Features. En ACM Computing Surveys (Vol. 55, Número 1). https://doi.org/10.1145/3486860
Anand, A., Bedi, J., Aggarwal, A., Khan, M. A., & Rida, I. (2024). Authenticating and securing healthcare records: A deep learning-based zero watermarking approach. En Image and Vision Computing (Vol. 145). https://doi.org/10.1016/j.imavis.2024.104975
Basile, C., Sutter, B. D., Canavese, D., Regano, L., & Coppens, B. (2023). Design, implementation, and automation of a risk management approach for man-at-the-End software protection. Computers & Security, 132, 103321. https://doi.org/10.1016/j.cose.2023.103321
Broeck, J. V. den, Coppens, B., & Sutter, B. D. (2022). Flexible software protection. Computers & Security, 116, 102636. https://doi.org/10.1016/j.cose.2022.102636
Chaudhuri, J., Bhattacharya, M., & Chakrabarty, K. (2025). Enhancing Analog IC Security Using Randomized Obfuscation Circuits. En IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (Vol. 44, Número 3, pp. 867-881). https://doi.org/10.1109/TCAD.2024.3466810
Corona-Fraga, P., Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, L. K., Perez-Meana, H., Portillo-Portillo, J., Olivares-Mercado, J., & García Villalba, L. J. (2025). Question–Answer Methodology for Vulnerable Source Code Review via Prototype-Based Model-Agnostic Meta-Learning. En Future Internet (Vol. 17, Número 1). https://doi.org/10.3390/fi17010033
Dileesh, E. D., & Shanthi, A. P. (2020). M-PIVAD - Virtual memory based approach against non-control data attacks. Computers & Security, 95, 101834. https://doi.org/10.1016/j.cose.2020.101834
Greco, C., Ianni, M., Guzzo, A., & Fortino, G. (2024). Enabling Obfuscation Detection in Binary Software through eXplainable AI. IEEE Transactions on Emerging Topics in Computing, 1-12. https://doi.org/10.1109/TETC.2024.3439884
Hashemi, M., Mohammadi, S., & Carlson, T. E. (2025). TOP: A Combined Logical and Physical Obfuscation Method for Securing Networks-on-Chip Against Reverse Engineering Attacks. IEEE Access, 13, 133438-133456. https://doi.org/10.1109/ACCESS.2025.3590967
Hataba, M., Sherif, A., & Elkhouly, R. (2022). Enhanced Obfuscation for Software Protection in Autonomous Vehicular Cloud Computing Platforms. En IEEE Access (Vol. 10, pp. 33943-33953). https://doi.org/10.1109/ACCESS.2022.3159249
Kim, H. Y., & Lee, D. H. (2024). CatchFuzz: Reliable active anti-fuzzing techniques against coverage-guided fuzzer. En Computers and Security (Vol. 143). https://doi.org/10.1016/j.cose.2024.103904
Lee, J.-S., Liu, C., Chen, Y.-C., Hung, W.-C., & Li, B. (2021). Robust 3D mesh zero-watermarking based on spherical coordinate and Skewness measurement. En Multimedia Tools and Applications (Vol. 80, Número 17, pp. 25757-25772). https://doi.org/10.1007/s11042-021-10878-0
Li, Y., Kang, F., Shu, H., Xiong, X., Sha, Z., & Sui, Z. (2022). COOPS: A Code Obfuscation Method Based on Obscuring Program Semantics. En Security and Communication Networks (Vol. 2022). https://doi.org/10.1155/2022/6903370
Mishra, D., Obaidat, M. S., Rana, S., Dharminder, D., Mishra, A., & Sadoun, B. (2021). Chaos-Based Content Distribution Framework for Digital Rights Management System. En IEEE Systems Journal (Vol. 15, Número 1, pp. 570-576). https://doi.org/10.1109/JSYST.2020.2977929
Norby, A., Rimal, B. P., & Brizendine, B. (2025). Measurement of Anti-Debugging Techniques on the Windows and Linux Operating Systems for the Intel x86_64 Architecture. IEEE Access, 13, 46568-46583. https://doi.org/10.1109/ACCESS.2025.3550009
Rauti, S., & Laato, S. (2024). Enhancing resilience in IoT cybersecurity: The roles of obfuscation and diversification techniques for improving the multilayered cybersecurity of IoT systems. En Data and Policy (Vol. 6). https://doi.org/10.1017/dap.2024.84
Rauti, S., Laurén, S., Mäki, P., Uitto, J., Laato, S., & Leppänen, V. (2021). Internal interface diversification as a method against malware. En Journal of Cyber Security Technology (Vol. 5, Número 1, pp. 15-40). https://doi.org/10.1080/23742917.2020.1813397
Regano, L., & Canavese, D. (2024). Automated Intel SGX Integration for Enhanced Application Security. En IEEE Access (Vol. 12, pp. 110312-110321). https://doi.org/10.1109/ACCESS.2024.3441240
Rodríguez Véliz, Y. N., Miguel, Hernández González, Anaisa, Lima, Roberto Sepúlveda, Musa. (2024). Validación de un modelo de protección basado en la ofuscación del código. Ingeniería Industrial, 45, 91-108.
Saxena, U. R., & Alam, T. (2022). Role based access control using identity and broadcast based encryption for securing cloud data. En Journal of Computer Virology and Hacking Techniques (Vol. 18, Número 3, pp. 171-182). https://doi.org/10.1007/s11416-021-00402-1
Suk, J. H., & Lee, D. H. (2020). VCF: Virtual Code Folding to Enhance Virtualization Obfuscation. IEEE Access, 8, 139161-139175. https://doi.org/10.1109/ACCESS.2020.3012684
Tang, Z., Yu, J., Chai, X., Ma, T., Gan, Z., & Wang, B. (2025). ImageShield: A responsibility-to-person blind watermarking mechanism for image datasets protection. En Applied Intelligence (Vol. 55, Número 1). https://doi.org/10.1007/s10489-024-06093-7
Xiong, X., Sha, Z., Shu, H., & Wei, R. (2025). Code obfuscation based on deep integration. En Computer Journal (Vol. 68, Número 12, pp. 1980-1995). https://doi.org/10.1093/comjnl/bxaf089
Yue, T., Zhang, F., Ning, Z., Wang, P., Zhou, X., Lu, K., & Zhou, L. (2024). Armor: Protecting Software Against Hardware Tracing Techniques. IEEE Transactions on Information Forensics and Security, 19, 4247-4262. https://doi.org/10.1109/TIFS.2024.3372816
Zhang, W., Xu, Z., Xiao, Y., & Xue, Y. (2022). Unleashing the power of pseudo-code for binary code similarity analysis. En Cybersecurity (Vol. 5, Número 1). https://doi.org/10.1186/s42400-022-00121-0
Zhou, Z., & Wang, C. (2023). Practical Anti-Fuzzing Techniques With Performance Optimization. IEEE Open Journal of the Computer Society, 4, 206-217. https://doi.org/10.1109/OJCS.2023.3301883