Generic placeholder image

Journal of Fuzzy Logic and Modeling in Engineering

Author(s): Mohd. Nadeem*, Masood Ahmad, Syed Anas Ansar, Prabhash Chandra Pathak, Rajeev Kumar and Raees Ahmad Khan

DOI: 10.2174/2666294902666230817162030

Cite As
Security Evaluation of Software by Using Fuzzy-TOPSIS through Quantum Criteria

Article ID: e170823219942 Pages: 11

  • * (Excluding Mailing and Handling)

Abstract

Quantum computer development attracts security experts in software. Software developers need to pay attention to the development of quantum computers in terms of software security. The security of software is at risk with the computation speed of quantum mechanisms in computing.

Background: Software security evaluation focuses on the fundamental security features of the software as well as the quantum enable security alternatives. The rapid development of a number of qubits in quantum computers makes the present security mechanism of software insecure. The software security evaluation is the most crucial part of surveying, controlling, and administering security in order to further improve the properties of safety.

Objective: It's crucial to understand that performing a security assessment early on in the development process can help you find bugs, vulnerabilities, faults, and attacks. In this quantitative study, the definition and use of the quantum computing security approach in software security will be covered. The cryptographic calculations had to secure our institutions based on computers and networks.

Methods: The Fuzzy Technique for Order Preference by Similarity to Ideal Situation (Fuzzy- TOPSIS) to quantitatively assess the rank of the quantum enable security alternatives with security factors.

Results: The Quantum Key Distribution [A2], the quantum technique of security approach, has got the top priority and quantum key distribution in GHz state [A6] got the least in the estimation of software security during the era of quantum computer by the neural network method of Fuzzy- TOPSIS.

Conclusion: The quantum mechanism of computing makes classical computing insecure. The security estimation of software makes developers focus on the quantum mechanism of security. The quantum mechanism of quantum key distribution is to make software secure.

Keywords: Quantum computing, software security, quantum algorithm, quantum security, fuzzy-topsis, quantum software security.

[1]
Walthall, R.; Dixit, S. Impact of quantum computing in aerospace. Aviat. Data Inform. Tech. SAE International, 2022.
[http://dx.doi.org/10.4271/EPR2022014]
[2]
Arute, F.; Arya, K.; Babbush, R. Quantum supremacy using a programmable superconducting processor, vol. 574, no. 7779, pp. 505-510, 2019. Nature, 2019, 574(7779), 505-510.
[http://dx.doi.org/10.1038/s41586-019-1666-5]
[3]
Möller, M.; Vuik, C. On the impact of quantum computing technology on future developments in high-performance scientific computing. Ethics Inf. Technol., 2017, 19(4), 253-269.
[http://dx.doi.org/10.1007/s10676-017-9438-0]
[4]
ITRC business impact report - ITRC Available from: https://www.idtheftcenter.org/publication/itrc-2022-business-impact-report/(accessed May 06, 2023)
[5]
Alyami, H.; Nadeem, M.; Alharbi, A.; Alosaimi, W.; Ansari, M.T.J.; Pandey, D.; Kumar, R.; Khan, R.A. The evaluation of software security through quantum computing techniques: A durability perspective. Appl. Sci., 2021, 11(24), 11784.
[http://dx.doi.org/10.3390/app112411784]
[6]
Mitra, S.; Jana, B.; Bhattacharya, S.; Pal, P.; Poray, J. Quantum cryptography: Overview, security issues and future challenges 4th International Conference on Opto-Electronics and Applied Optics (Optronix), 2018.Kolkata, India
[http://dx.doi.org/10.1109/OPTRONIX.2017.8350006]
[7]
Ruparelia, N.B. Software development lifecycle models. Softw. Eng. Notes, 2010, 35(3), 8-13.
[http://dx.doi.org/10.1145/1764810.1764814]
[8]
Lee, W.K.; Seo, H.; Zhang, Z.; Hwang, S.O. Tensorcrypto: High throughput acceleration of lattice-based cryptography using tensor core on GPU. IEEE Access, 2022, 10, 20616-20632.
[http://dx.doi.org/10.1109/ACCESS.2022.3152217]
[9]
Shor, P.W. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput., 1997, 26(5), 1484-1509.
[http://dx.doi.org/10.1137/S0097539795293172]
[10]
Garcia Cid, M.I.; Álvaro González, J.; Ortíz Martín, L.; Del Río Gómez, D. Disruptive quantum safe technologies ACM Int. Conf. Proceeding Ser, 23 August 2022 New York, NY, United States2022.
[http://dx.doi.org/10.1145/3538969.3544484]
[11]
Banegas, G.; Zandberg, K.; Baccelli, E.; Herrmann, A.; Smith, B. Quantum-resistant software update security on low-power networked embedded devices In: Applied Cryptography and Network Security; Ateniese,, G.; Venturi, D., Eds.; Springer: Cham, ACNS, 2022; 13269, pp. 872-891. Lecture Notes in Computer Science
[http://dx.doi.org/10.1007/978-3-031-09234-3_43]
[12]
Alyami, H.; Nadeem, M.; Alosaimi, W.; Alharbi, A.; Kumar, R.; Kumar Gupta, B.; Agrawal, A.; Ahmad Khan, R. Analyzing the data of software security life-span: Quantum computing era. Intell. Autom. Soft. Comp., 2022, 31(2), 707-716.
[http://dx.doi.org/10.32604/iasc.2022.020780]
[13]
Agrawal, A.; Alenezi, M.; Kumar, R.; Khan, R.A. Measuring the sustainable-security of web applications through a fuzzy-based integrated approach of AHP and TOPSIS. IEEE Access, 2019, 7, 153936-153951.
[http://dx.doi.org/10.1109/ACCESS.2019.2946776]
[14]
Phaphoom, N.; Wang, X.; Samuel, S.; Helmer, S.; Abrahamsson, P. A survey study on major technical barriers affecting the decision to adopt cloud services. J. Syst. Softw., 2015, 103, 167-181.
[http://dx.doi.org/10.1016/j.jss.2015.02.002]
[15]
Hwang, S.; Park, J.; Yoon, K.; Jun, K. A trusty digital rights management in content distribution environment 2023. Available from: http://dpnm.postech.ac.kr/papers/DSOM/03/27-eoktrirnhw.pdf Accessed: May 08, 2023
[16]
Alzahrani, F.A.; Ahmad, M.; Nadeem, M.; Kumar, R.; Khan, R.A. Integrity assessment of medical devices for improving hospital services. Comput. Mater. Continua, 2021, 67(3)
[http://dx.doi.org/10.32604/cmc.2021.014869]
[17]
Nadeem, M. Multi-level hesitant fuzzy based model for usable-security assessment Intell. Autom. Soft Comput., 2022, 31(1)
[http://dx.doi.org/10.32604/iasc.2022.019624]
[18]
Azzaoui, A.E.L.; Sharma, P.K.; Park, J.H. Blockchain-based delegated quantum cloud architecture for medical big data security. J. Netw. Comput. Appl., 2022, 198, 103304.
[http://dx.doi.org/10.1016/j.jnca.2021.103304]
[19]
Bose, R.; Johnson, H.T. Coulomb interaction energy in optical and quantum computing applications of self-assembled quantum dots. Microelectron. Eng., 2004, 75(1), 43-53.
[http://dx.doi.org/10.1016/j.mee.2003.11.008]
[20]
Misra, S.C. Modeling design/coding factors that drive maintainability of software systems. Softw. Qual. J., 2005, 13, 297-320.
[http://dx.doi.org/10.1007/s11219-005-1754-7]
[21]
Midilli, A.; Dincer, I.; Ay, M. Green energy strategies for sustainable development. Energy Policy, 2006, 34(18), 3623-3633.
[http://dx.doi.org/10.1016/j.enpol.2005.08.003]
[22]
Abdullah, M.A.; Muttaqi, K.M.; Agalgaonkar, A.P. Sustainable energy system design with distributed renewable resources considering economic, environmental and uncertainty aspects. Renew. Energy, 2015, 78, 165-172.
[http://dx.doi.org/10.1016/j.renene.2014.12.044]
[23]
Li, N. Research on diffie-hellman key exchange protocol 2nd International Conference on Computer Engineering and Technology, Chengdu, China2010.
[http://dx.doi.org/10.1109/ICCET.2010.5485276]
[24]
Hellman, D.; Aditya Kakaraparthi, K.; Karthick, V.; Yuwen, Wang; Mogos, G.; Kumar, C.; Raj Vincen, M.D.P. Enhanced diffie-hellman algorithm for reliable key exchange IOP Conf. Ser. Mater. Sci. Eng., 2017, 263, p. 042015.
[http://dx.doi.org/10.1088/1757-899X/263/4/042015]
[25]
Rashid, M.; Kumar, H.; Khan, S.Z.; Bahkali, I.; Alhomoud, A.; Mehmood, Z. Throughput/area optimized architecture for elliptic-curve diffie-hellman protocol. Appl. Sci., 2022, 12(8), 4091.
[http://dx.doi.org/10.3390/app12084091]
[26]
Bacsardi, L. Using quantum computing algorithms in future satellite communication Acta Astronaut., 2005, 57(2-8), 224-229.
[http://dx.doi.org/10.1016/j.actaastro.2005.03.023]
[27]
Fang, J. Improved polar-code-based efficient post-processing algorithm for quantum key distribution. Sci. Rep., 2022, 12(1), 10155.
[http://dx.doi.org/10.1038/s41598-022-14145-6]
[28]
Adu-Kyere, A.; Nigussie, E.; Isoaho, J. Quantum key distribution: Modeling and simulation through BB84 protocol using python3. Sensors, 2022, 22(16), 6284.
[http://dx.doi.org/10.3390/s22166284]
[29]
Mishima, K.; Tokumo, K.; Yamashita, K. Quantum computing using molecular electronic and vibrational states. Chem. Phys., 2008, 343(1), 61-75.
[http://dx.doi.org/10.1016/j.chemphys.2007.10.027]
[30]
Rycerz, K.; Patrzyk, J.; Patrzyk, B.; Bubak, M. Teaching quantum computing with the quide simulator. Procedia Comput. Sci., 2015, 51, 1724-1733.
[http://dx.doi.org/10.1016/j.procs.2015.05.374]
[31]
Hooyberghs, J. Deutsch-jozsa algorithm.Introd. Microsoft Quantum Comput. Dev; O'Reilly, 2022, pp. 233-270.
[http://dx.doi.org/10.1007/978-1-4842-7246-6_9]
[32]
Qiu, D.; Zheng, S. Revisiting deutsch-jozsa algorithm. Inf. Comput., 2020, 275, 104605.
[http://dx.doi.org/10.1016/j.ic.2020.104605]
[33]
Petrosyan, D.; Zhang, P. Quantum attacks on sum of even–mansour construction with linear key schedules. Entropy, 2022, 24(2), 153.
[http://dx.doi.org/10.3390/e24020153]
[34]
Grassl, M.; Langenberg, B.; Roetteler, M.; Steinwandt, R. Applying grover’s algorithm to AES: Quantum resource estimates Post-Quantum Cryptography. springer, 2016, 29-43.
[http://dx.doi.org/10.1007/978-3-319-29360-8_3]
[35]
Nagata, K.; Nakamura, T.; Farouk, A. Quantum cryptography based on the deutsch-jozsa algorithm. Int. J. Theor. Phys., 2017, 56(9), 2887-2897.
[http://dx.doi.org/10.1007/s10773-017-3456-x]
[36]
Abidin, S.; Swami, A.; Ramirez-Asís, E.; Alvarado-Tolentino, J.; Maurya, R.K.; Hussain, N. Quantum cryptography technique: A way to improve security challenges in mobile cloud computing (MCC). Mater. Today Proc., 2022, 51, 508-514.
[http://dx.doi.org/10.1016/j.matpr.2021.05.593]
[37]
Hwang, C. Multiple attribute decision making: Methods and applications: A state-of-the-art survey Available from: https://cir.nii.ac.jp/crid/1130000796669247872(Accessed: May 08, 2023)
[38]
Yoon, K.; Hwang, C. Multiple attribute decision making: An introduction In: Quantitative Applications in the Social Sciences; , 1995. SAGE Publications, Inc
[39]
Yoon, K.; Sedaghat, M. portfolio selection by the axiom of choice: Post mean-variance analysis. I. J. of Opers. and Quant. Management, 2020, 26(2), 303-318.
[40]
Akram, M.; Luqman, A.; Alcantud, J.C.R. Risk evaluation in failure modes and effects analysis: Hybrid topsis and electre i solutions with pythagorean fuzzy information. Neural Comput. Appl., 2021, 33(11), 5675-5703.
[http://dx.doi.org/10.1007/s00521-020-05350-3]
[41]
Hwang, S.; Yoon, K.; Jun, K. Modeling and implementation of digital rights. J. Sys. Soft., 2004, 73(3), 553-549. Available from: https://www.sciencedirect.com/science/article/pii/S0164121203002899(Accessed: May 08, 2023)