Bio-inspired combinatorial models for encryption algorithms
Yogita Deepak Sinkaryogita.sinkar@vpkbiet.orgDepartment of Artificial Intelligence and Data ScienceVidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and TechnologyBaramati, Pune, Maharashtra, IndiaView full profile → , *Putta ChandanaCorresponding authorputtachandana@proton.meCentre for Multidisciplinary ResearchAnurag UniversityHyderabad, Telangana, 500088, India0009-0001-1370-5048View full profile → , Soumitra Dassoumitra_das@yahoo.comDepartment of Computer EngineeringIndira College of Engineering & ManagementPune, Maharashtra, 410506, IndiaView full profile → , Vikas Singhvikas.singh@rungta.orgDepartment of Computer Science and EngineeringRungta College of Engineering and TechnologyRungta International Skills UniversityBhilai, Chhattisgarh, 490024, IndiaView full profile → , Chetansinh R. Vaghelacrvaghela.maths.gsc@gokuluniversity.ac.inFaculty of ScienceGokul Global UniversitySiddhpur, Gujarat, 384151, IndiaView full profile → , Rahul A. Padgilwarrahul.padgilwar1@vit.eduDepartment of Engineering Science and HumanitiesVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Ashok Bhansaliashok.bhansali@gla.ac.inDepartment of Computer Engineering & ApplicationsGLA UniversityMathura, Uttar Pradesh, 281406, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3065
- Pages:
- 4457–4471
Abstract
The growing use of digital communication systems, cloud infrastructure, and Internet of things (IoT) environments has aggravated the necessity to have strong and dynamic encryption schemes to protect sensitive information. Although effective, the traditional cryptographic algorithms are increasingly challenged by the increasing cyber threats and new computation models like quantum computing. Bio-inspired intelligence combined with the combinatorial mathematical models is in this context a promising way of increasing the strength of encryption and flexibility. This paper presents a new bio-inspired combinatoric encryption system that builds upon permutation based key space based upon symmetric groups and incorporates optimization methods such as Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization and Artificial Immune Systems. The aim of the research is to develop a dynamic and scalable encryption model that can generate high-entropy keys, as well as resist advanced cryptanalytic attacks. The approach includes building a key space, using bio-inspired optimization to generate ad-hoc keys, and using permutation based encryption with nonlinear transformations. Some of the most important security and performance measures against which the proposed system is tested are the entropy, avalanche effect, time of encryption/decryption, and the complexity involved in the computation. Experimental and comparative data prove that the given framework reaches the almost ideal entropy (7.99) and the best avalanche effect (0.498), which are better in adaptability and security strength compared to traditional encryption schemes AES and RSA.
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References
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