P4-assisted context-aware encryption framework for secure packet transmission in active SDN networks
Prashant Vatsprashant.vats@jaipur.manipal.eduDepartment of Computer Science and EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Sandeep Kumar Budhanisandeepbudhani13@gmail.comDepartment of Computer ApplicationsSchool of ComputingGraphic Era Hill UniversityBhimtal Campus, Bhimtal, Uttarakhand, 263136, IndiaView full profile → , Surabhi Shankersurabhi.shanker@krmangalam.edu.inCentre of Excellence - Cyber SecuritySchool of Engineering and TechnologyK. R. Mangalam UniversityGurugram, Haryana, 122103, IndiaView full profile → , Devesh Kumar Srivastavadevesh.srivastava@jaipur.manipal.eduDepartment of Information TechnologyManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Madan Lal Sainimadan.e13485@cumail.inDepartment of Computer Science and EngineeringApex Institute of Technology (AIT)Chandigarh UniversityMohali, Punjab, 140413, IndiaView full profile → , *Ashok Kumar SainiCorresponding authorashok.saini@jaipur.manipal.eduDepartment of Computer Science and EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 14 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2637
- Pages:
- 3001–3009
Abstract
The rapid evolution of Software-Defined Networking (SDN) has transformed modern communication systems by enabling centralized and adaptive control of data flows. However, the increasing dynamism of SDN environments poses new challenges to maintain data confidentiality and cryptographic integrity. This paper presents a discrete cryptographic framework for Context-Aware Packet Encryption in Active SDN Networks Using P4-Based Code Injection. The proposed model employs selective, context-driven encryption at the packet level, utilizing discrete mathematical structures to dynamically determine encryption paths and security mappings. By integrating cryptographic primitives directly within the data plane, the framework achieves fine-grained protection while reducing redundancy and latency commonly observed in network-wide encryption mechanisms. Analytical evaluation demonstrates that the proposed approach ensures robust confidentiality, efficient key utilization, and optimal resource allocation under varying network conditions. The results affirm that discrete cryptographic methods can substantially enhance the efficiency and resilience of SDN-based communication systems against evolving security threats.
Keywords
Subject Classifications
References
[1] Kiran, B. D. Parameshachari, H. T. Channabasappa, and S. L. Ullo, “Analysis and computation of encryption techniques to enhance security of medical images,” IOP Conference Series: Materials Science and Engineering, vol. 925, no. 1, Art. no. 012028 (2020), doi: 10.1088/1757-899X/925/1/012028.
[2] T. Sun and X. Wang, “Medical image security authentication method based on wavelet reconstruction and fractal dimension,” International Journal of Distributed Sensor Networks, vol. 17, no. 4 (2021), doi: 10.1177/15501477211014132.
[3] K. Jain and A. Aji, “Medical image encryption scheme using multiple chaotic maps,” Pattern Recognition Letters, vol. 152, pp. 356–364 (2021), doi: 10.1016/j.patrec.2021.10.033.
[4] P. Khare and V. K. Srivastava, “A secured and robust medical image watermarking approach for protecting integrity of medical images,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 2, Art. no. e3918 (2021), doi: 10.1002/ett.3918.
[5] E. K. Wang, C.-M. Chen, M. M. Hassan, and A. Almogren, “A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain,” Future Generation Computer Systems, vol. 108, pp. 135–144 (Jul. 2020), doi: 10.1016/j.future.2020.02.054.
[6] C. Ghandour, W. El-Shafai, and E.-S. M. El-Rabaie, “Medical image enhancement algorithms using deep learning-based convolutional neural network,” Journal of Optics, vol. 52, no. 19, pp. 1–11 (Jan. 2023), doi: 10.1007/s12596-022-01078-6.
[7] T. Dhar, N. Dey, S. Borra, and R. S. Sherratt, “Challenges of deep learning in medical image analysis—Improving explainability and trust,” IEEE Transactions on Technology and Society, vol. 4, no. 1, pp. 68–75 (Mar. 2023), doi: 10.1109/TTS.2023.3234203.
[8] M. K. Hasan, S. Islam, R. Sulaiman, S. Khan, A. H. A. Hashim, S. Habib, M. Z. Islam, and S. Alyahya, “Lightweight encryption technique to enhance medical image security on Internet of Medical Things applications,” IEEE Access, vol. 9, pp. 47731–47742 (2021), doi: 10.1109/ACCESS.2021.3061710.
[9] Z. Li, M. Dong, S. Wen, X. Hu, P. Zhou, and Z. Zeng, “CLU-CNNs: Object detection for medical images,” Neurocomputing, vol. 350, pp. 53–59 (2019), doi: 10.1016/j.neucom.2019.04.028.
[10] R. Natarajan, G. H. Lokesh, F. Flammini, A. Premkumar, V. K. Venkatesan, and S. K. Gupta, “A novel framework on security and energy enhancement based on Internet of Medical Things for Healthcare 5.0,” Infrastructures, vol. 8, no. 2, Art. no. 22 (2023), doi: 10.3390/infrastructures8020022.
[11] B. L. V. S. Aditya and S. N. Mohanty, “Design of an efficient model for fake profile detection on social media using advanced feature engineering and deep learning techniques,” Journal of Information and Optimization Sciences, vol. 46, no. 6, pp. 1803–1810 (2025), doi: 10.47974/JIOS-2009.
[12] S. D. Bahinipati and B. K. Pattanayak, “A novel blockchain-enabled smart contract for smart city e-governance ecosystem,” Journal of Information and Optimization Sciences, vol. 46, no. 6, pp. 1831–1840 (2025), doi: 10.47974/JIOS-2012.
[13] Z. S. Alsham, E. Bahçekapılı, and A. Ayaz, “Trends in IoT applications in smart campuses: A topic modeling approach,” COLLNET Journal of Scientometrics and Information Management, vol. 19, no. 1, pp. 21–40 (2025), doi: 10.47974/CJSIM-2024-017.
[14] W. Sripanya, W. Rungrottheera, and P. Hyunsin, “Fourier series analysis and computation based on function characteristics,” Journal of Interdisciplinary Mathematics, vol. 28, no. 6, pp. 2109–2120 (2025), doi: 10.47974/JIM-2352.




