Salp Swarm Algorithm to solve Cryptographic Key Generation problem for Cloud computing

  • Waseem Kaleem Department of Computer Science, Aligarh Muslim University, Aligarh-202002, Uttar Pradesh, India https://orcid.org/0009-0006-1514-6119
  • Mohammad Sajid Department of Computer Science, Aligarh Muslim University, Aligarh-202002, Uttar Pradesh, India https://orcid.org/0000-0001-8822-5332
  • Ranjit Rajak Department of Computer Science and Applications, Dr. Harisingh Gour Central University, Sagar-470003, Madhya Pradesh, India https://orcid.org/0000-0003-2746-3278
Keywords: Cryptography, randomness, key generation, Shannon entropy, transfer function, Quantization method

Abstract

Cryptographic keys are long strings of random bits generated using specialized algorithms and help secure data by making it unpredictable to any adversary. Cryptographic keys are used in various cryptographic algorithms in many domains, i.e., Cloud computing, Internet-of-Things (IoT), Fog computing, and others. The key generation algorithms are essential in cryptographic data encryption and decryption algorithms. This work proposed a cryptographic key generation algorithm based on Shannon entropy and the Salp Swarm algorithm (SSA) for generating randomized keys. The proposed Cryptographic Key Generation algorithm utilizes the dynamic movement of salps to create high-quality, robust, and randomized keys against attacks. The transfer function and quantization method convert a salp into a cryptographic key. The proposed Cryptographic Key Generation algorithm has been evaluated on four transfer functions against three state-of-the-art swarm intelligence metaheuristics, i.e., particle swarm optimization, BAT, and grey wolf optimization algorithms. The keys of eight different bit lengths, i.e., 512, 256, 192, 128, 96, 80, 64, were generated and evaluated due to their applications in the different encryption algorithms, i.e., AES, DES, PRESENT, SIMON, SPECK, and 3DES. The simulation study confirms that the proposed key generation algorithm effectively produces secure cryptographic keys.

References

Ahsan, M. M., Gupta, K. D., Nag, A. K., Poudyal, S., Kouzani, A. Z., & Mahmud, M. A. P. (2020). Applications and evaluations of bio-inspired approaches in cloud security: A review. IEEE Access, 8, 180799-180814. https://doi.org/10.1109/ACCESS.2020.3027841

Alouffi, B., Hasnain, M., Alharbi, A., Alosaimi, W., Alyami, H., & Ayaz, M. (2021). A systematic literature review on cloud computing security: threats and mitigation strategies. IEEE Access, 9, 57792-57807. https://doi.org/10.1109/ACCESS.2021.3073203.

Ali, M., Khan, S. U., & Vasilakos, A. V. (2015). Security in cloud computing: Opportunities and challenges. Information Sciences, 305, 357–383. https://doi.org/10.1016/j.ins.2015.01.025.

Beheshti, Z. (2021). UTF: Upgrade transfer function for binary meta-heuristic algorithms. Applied Soft Computing, 106, 107346. https://doi.org/10.1016/j.asoc.2021.107346

Bhardwaj, A., Subrahmanyam, G., Avasthi, V., & Sastry, H. (2016). Security Algorithms for Cloud Computing. Procedia Computer Science, 85, 535–542. https://doi.org/10.1016/j.procs.2016.05.215.

Cook, A., Robinson, M., Ferrag, M., Leandros, H., & Ying, J. (2018). Internet of Cloud: Security and Privacy Issues. IEEE Internet Computing, 15(1), 73-76.

Crawford, B., Soto, R., Astorga, G., García, J., Castro, C., & Paredes, F. (2017). Putting continuous metaheuristics to work in binary search spaces. Complexity, 8404231.

Del Ser, J., Osaba, E., Dondo, J., López-Guede, J. M., & Molina, D. (2018). Bio-Inspired Computation: Where We Stand and What's Next. Complexity, 2018, 1-16. https://doi.org/10.1155/2018/9343095.

Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., & Cosar, A. (2019). A survey on new generation metaheuristic algorithms. Computers & Industrial Engineering, 137, 106040.

Gartner. (2021). Forecast: IT Services for IoT, Worldwide, 2019-2025. [Online]. Available: https://www.gartner.com/en/documents/4004741

Gupta, M., Gupta, K. K., & Shukla, P. K. (2021). Session key-based fast, secure, and lightweight image encryption algorithm. Multimedia Tools and Applications, 80(7), 10391–10416. https://doi.org/10.1007/s11042-020-10116-z.

Gupta, R. K., Almuzaini, K. K., Pateriya, R. K., Shah, K., Shukla, P. K., & Akwafo, R. (2022). An improved secure key generation using enhanced identity-based encryption for cloud computing in large-scale 5G. Wireless Communications and Mobile Computing, 2022, 7291250. https://doi.org/10.1155/2022/7291250.

Hashizume, K., Rosado, D. G., Fernández-Medina, E., & Fernández, E. B. (2013). An analysis of security issues for cloud computing. Journal of Internet Services and Applications, 4, 5.

Jawed, M. S., & Sajid, M. (2022). A comprehensive survey on cloud computing: architecture, tools, technologies, and open issues. International Journal of Cloud Applications and Computing, 12(1), 1–33. https://doi.org/10.4018/IJCAC.308277.

Jawed, M.S., & Sajid, M. (2022). XECryptoGA: A Metaheuristic algorithm-based Block Cipher to Enhance the Security Goals. Evolutionary Systems. https://doi.org/10.1007/s12530-022-09462-0.

Kalsi, S., Kaur, H., & Chang, V. (2017). DNA cryptography and deep learning using genetic algorithm with NW algorithm for key generation. Journal of Medical Systems, 42(1). https://doi.org/10.1007/s10916-017-0851-z.

Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, IV, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968.

Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, Orlando, FL, USA, 5, 4104–4108.

Khalid, T., Abbasi, M.A.K., Zuraiz, M., Khan, A.N., Ali, M., Ahmad, R.W., Rodrigues, J.J.P.C., Aslam, M. (2021). A survey on privacy and access control schemes in fog computing. International Journal of Communication Systems, 34(2), e4181. https://doi.org/10.1002/dac.4181.

Krishna, G. J., Ravi, V., & Bhattu, S. N. (2018). Key generation for plain text in stream cipher via bi-objective evolutionary computing. Applied Soft Computing, 70, 301–317. https://doi.org/10.1016/j.asoc.2018.05.025.

Lanza-Gutierrez, J. M., Crawford, B., Soto, R., Berrios, N., Gomez-Pulido, J. A., & Paredes, F. (2017). Analyzing the effects of binarization techniques when solving the set covering problem through swarm optimization. Expert Systems with Applications, 70, 67–82. https://doi.org/10.1016/j.eswa.2016.10.054

Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191. https://doi.org/10.1016/j.advengsoft.2017.07.002

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007

Osaba, E., Villar-Rodriguez, E., Del Ser, J., Nebro, A. J., Molina, D., LaTorre, A., Suganthan, P. N., Coello-Coello, C. A., & Herrera, F. (2021). A tutorial on the design, experimentation, and application of metaheuristic algorithms to real-world optimization problems. Swarm and Evolutionary Computation, 64, 100888. https://doi.org/10.1016/j.swevo.2021.100888

Sicari, A. S., Rizzardi, A., & Coen-Porisini, A. (2022). Insights into security and privacy towards fog computing evolution. Computers & Security, 120, 102822. https://doi.org/10.1016/j.cose.2022.102822.

Singh, A., & Chatterjee, K. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79, 88–115. https://doi.org/10.1016/j.jnca.2016.11.027.

Subramanian, E. K., & Tamilselvan, L. (2020). Elliptic curve Diffie-Hellman cryptosystem in big data cloud security. Cluster Computing, pp.1–11. https://doi.org/10.1007/s10586-020-03069-3

Sun, Y., Lin, F., & Zhang, N. (2018). A security mechanism based on evolutionary game in fog computing. Saudi Journal of Biological Sciences, 25(2), 237–241. https://doi.org/10.1016/j.sjbs.2017.09.010.

Tabrizchi, H., & Kuchaki Rafsanjani, M. (2020). A survey on security challenges in cloud computing: issues, threats, and solutions. Journal of Supercomputing, 76, 9493–9532. https://doi.org/10.1007/s11227-020-03213-1

Tahir, M., Sardaraz, M., Mehmood, Z., & Muhammad, S. (2021). CryptoGA: a cryptosystem based on genetic algorithm for cloud data security. Cluster Computing, 24(2), 739–752. https://doi.org/10.1007/s10586-020-03157-4.

Thabit, F., Alhomdy, S., & Jagtap, S. (2021). A new data security algorithm for cloud computing based on genetics techniques and logical-mathematical functions. International Journal of Intelligent Networks, 2, 18–33. https://doi.org/10.1016/j.ijin.2021.03.001

Thabit, F., Alhomdy, S., Al-ahdal A.H.A., Jagtap, S. (2021), A new lightweight cryptographic algorithm for enhancing data security in cloud computing. Global Transitions Proceedings, 2(1), 91–99. https://doi.org/10.1016/j.gltp.2021.01.013.

Wang, T., Zhou, J., Chen, X., Wang, G., Liu, A., & Liu, Y. (2018). A three-layer privacy-preserving cloud storage scheme based on computational intelligence in fog computing. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 3–12. https://doi.org/10.1109/TETCI.2017.2764109.

Yang, X. S. (2010). A New Metaheuristic Bat-Inspired Algorithm. In Nature Inspired Cooperative Strategies for Optimization (NISCO 2010). Studies in Computational Intelligence, 284, 65–74.

Published
2023-07-30
How to Cite
Kaleem, W., Sajid, M., & Rajak, R. (2023). Salp Swarm Algorithm to solve Cryptographic Key Generation problem for Cloud computing. International Journal of Experimental Research and Review, 31(Spl Volume), 85-97. https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.009