Skip to main content
Log in

DNACDS: Cloud IoE big data security and accessing scheme based on DNA cryptography

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

The Internet of Everything (IoE) based cloud computing is one of the most prominent areas in the digital big data world. This approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the cloud. The IoE-based cloud computing services are located at remote locations without the control of the data owner. The data owners mostly depend on the untrusted Cloud Service Provider (CSP) and do not know the implemented security capabilities. The lack of knowledge about security capabilities and control over data raises several security issues. Deoxyribonucleic Acid (DNA) computing is a biological concept that can improve the security of IoE big data. The IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol (StS KAP) and Feistel cipher algorithms. This paper proposed a DNA-based cryptographic scheme and access control model (DNACDS) to solve IoE big data security and access issues. The experimental results illustrated that DNACDS performs better than other DNA-based security schemes. The theoretical security analysis of the DNACDS shows better resistance capabilities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Lakhan A, Mohammed M A, Abdulkareem K H, Jaber M M, Nedoma J, Martinek R, Zmij P. Delay optimal schemes for internet of things applications in heterogeneous edge cloud computing networks. Sensors, 2022, 22(16): 5937

    Article  Google Scholar 

  2. Gunal M M, Karatas M. Industry 4.0, digitisation in manufacturing, and simulation: a review of the literature. In: Gunal M M, ed. Simulation for Industry 4.0. Cham: Springer, 2019, 19–37

    Chapter  Google Scholar 

  3. Mahmood T, Ali Z. Prioritized muirhead mean aggregation operators under the complex single-valued neutrosophic settings and their application in multi-attribute decision-making. Journal of Computational and Cognitive Engineering, 2022, 1(2): 56–73

    Google Scholar 

  4. Snyder T, Byrd G. The internet of everything. Computer, 2017, 50(6): 8–9

    Article  Google Scholar 

  5. DeNardis L. The Internet in Everything. New Haven: Yale University Press, 2020

    Book  Google Scholar 

  6. Karatas M, Eriskin L, Deveci M, Pamucar D, Garg H. Big data for healthcare industry 4.0: Applications, challenges and future perspectives. Expert Systems with Applications, 2022, 200: 116912

    Article  Google Scholar 

  7. Tripathi S, De S. Pathway and future of IoE in smart cities: challenges of big data and energy sustainability. In: Jindal A, Kumar N, Aujla G S, eds. Internet of Energy for Smart Cities. Boca Raton: CRC Press, 2021, 277–302

    Chapter  Google Scholar 

  8. Lakhan A, Mohammed M A, Rashid A N, Kadry S, Abdulkareem K H, Nedoma J, Martinek R, Razzak I. Restricted Boltzmann machine assisted secure serverless edge system for internet of medical things. IEEE Journal of Biomedical and Health Informatics, 2022

  9. Lakhan A, Mohammed M A, Ibrahim D A, Abdulkareem K H. Bio-inspired robotics enabled schemes in blockchain-fog-cloud assisted IoMT environment. Journal of King Saud University-Computer and Information Sciences, 2021

  10. Singh A, Chatterjee K. Cloud security issues and challenges: a survey. Journal of Network and Computer Applications, 2017, 79: 88–115

    Article  Google Scholar 

  11. Pavithran P, Mathew S, Namasudra S, Srivastava G. A novel cryptosystem based on DNA cryptography, hyperchaotic systems and a randomly generated Moore machine for cyber physical systems. Computer Communications, 2022, 188: 1–12

    Article  Google Scholar 

  12. Zhang Q, Cheng L, Boutaba R. Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 2010, 1(1): 7–18

    Article  Google Scholar 

  13. Chang V, Kuo Y H, Ramachandran M. Cloud computing adoption framework: a security framework for business clouds. Future Generation Computer Systems, 2016, 57: 24–41

    Article  Google Scholar 

  14. Yu S, Wang C, Ren K, Lou W. Achieving secure, scalable, and fine-grained data access control in cloud computing. In: Proceedings of the 29th Conference on Information Communications. 2010, 534–542

  15. Namasudra S, Sharma S, Deka G C, Lorenz P. DNA computing and table based data accessing in the cloud environment. Journal of Network and Computer Applications, 2020, 172: 102835

    Article  Google Scholar 

  16. Agrawal N, Tapaswi S. Defense mechanisms against DDoS attacks in a cloud computing environment: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 2019, 21(4): 3769–3795

    Article  Google Scholar 

  17. Sirichotedumrong W, Kiya H. Visual security evaluation of learnable image encryption methods against ciphertext-only attacks. In: Proceedings of 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). 2020, 1304–1309

  18. Vasko F J, Lu Y, McNally B. A simple methodology that efficiently generates all optimal spanning trees for the cable-trench problem. Journal of Computational and Cognitive Engineering, 2022, 1(1): 13–20

    Google Scholar 

  19. Wani A, Revathi S, Khaliq R. SDN-based intrusion detection system for IoT using deep learning classifier (IDSIoT-SDL). CAAI Transactions on Intelligence Technology, 2021, 6(3): 281–290

    Article  Google Scholar 

  20. Chen Z. Research on internet security situation awareness prediction technology based on improved RBF neural network algorithm. Journal of Computational and Cognitive Engineering, 2022, 1(3): 103–108

    MathSciNet  Google Scholar 

  21. Mohindru G, Mondal K, Banka H. Different hybrid machine intelligence techniques for handling IoT-based imbalanced data. CAAI Transactions on Intelligence Technology, 2021, 6(4): 405–416

    Article  Google Scholar 

  22. Rakotondravony N, Taubmann B, Mandarawi W, Weishäupl E, Xu P, Kolosnjaji B, Protsenko M, De Meer H, Reiser H P. Classifying malware attacks in IaaS cloud environments. Journal of Cloud Computing, 2017, 6(1): 26

    Article  Google Scholar 

  23. Doreswamy, Hooshmand M K, Gad I. Feature selection approach using ensemble learning for network anomaly detection. CAAI Transactions on Intelligence Technology, 2020, 5(4): 283–293

    Article  Google Scholar 

  24. Namasudra S, Deka G C. Advances of DNA Computing in Cryptography. Boca Raton: CRC Press, 2018

    Book  MATH  Google Scholar 

  25. Sheela S J, Suresh K V, Tandur D. A novel audio cryptosystem using chaotic maps and DNA encoding. Journal of Computer Networks and Communications, 2017, 2017: 2721910

    Article  Google Scholar 

  26. Mondal B, Mandal T. A light weight secure image encryption scheme based on chaos & DNA computing. Journal of King Saud University-Computer and Information Sciences, 2017, 29(4): 499–504

    Article  Google Scholar 

  27. Clelland C T, Risca V, Bancroft C. Hiding messages in DNA microdots. Nature, 1999, 399(6736): 533–534

    Article  Google Scholar 

  28. Leier A, Richter C, Banzhaf W, Rauhe H. Cryptography with DNA binary strands. Biosystems, 2000, 57(1): 13–22

    Article  Google Scholar 

  29. Tanaka K, Okamoto A, Saito I. Public-key system using DNA as a one-way function for key distribution. Biosystems, 2005, 81(1): 25–29

    Article  Google Scholar 

  30. Ahmed U, Lin J C W, Srivastava G. Privacy-preserving deep reinforcement learning in vehicle Ad Hoc networks. IEEE Consumer Electronics Magazine, 2022, 11(6): 41–48

    Article  Google Scholar 

  31. Lin J C W, Srivastava G, Zhang Y, Djenouri Y, Aloqaily M. Privacy-preserving multiobjective sanitization model in 6G IoT environments. IEEE Internet of Things Journal, 2021, 8(7): 5340–5349

    Article  Google Scholar 

  32. Lakhan A, Mohammed M A, Nedoma J, Martinek R, Tiwari P, Vidyarthi A, Alkhayyat A, Wang W. Federated-learning based privacy preservation and fraud-enabled blockchain IoMT system for healthcare. IEEE Journal of Biomedical and Health Informatics, 2022

  33. Kaushik S, Gandhi C. Ensure hierarchal identity based data security in cloud environment. International Journal of Cloud Applications and Computing, 2019, 9(4): 21–36

    Article  Google Scholar 

  34. Kaufman L M. Data security in the world of cloud computing. IEEE Security & Privacy, 2009, 7(4): 61–64

    Article  Google Scholar 

  35. Sood S K. A combined approach to ensure data security in cloud computing. Journal of Network and Computer Applications, 2012, 35(6): 1831–1838

    Article  Google Scholar 

  36. Arockiam L, Monikandan S. Efficient cloud storage confidentiality to ensure data security. In: Proceedings of 2014 International Conference on Computer Communication and Informatics. 2014, 1–5

  37. Manogaran G, Thota C, Kumar M V. MetaCloudDataStorage architecture for big data security in cloud computing. Procedia Computer Science, 2016, 87: 128–133

    Article  Google Scholar 

  38. Attasena V, Darmont J, Harbi N. Secret sharing for cloud data security: a survey. The VLDB Journal, 2017, 26(5): 657–681

    Article  Google Scholar 

  39. Akhil K M, Kumar M P, Pushpa B R. Enhanced cloud data security using AES algorithm. In: Proceedings of 2017 International Conference on Intelligent Computing and Control (I2C2). 2017, 1–5

  40. Kalpana P, Singaraju S. Data security in cloud computing using RSA algorithm. International Journal of Research in Computer & Communication Technology, 2012, 1(4): 143–146

    Google Scholar 

  41. Sugumaran M, Murugan B B, Kamalraj D. An architecture for data security in cloud computing. In: Proceedings of 2014 World Congress on Computing and Communication Technologies. 2014, 252–255

  42. Almutairi A, Sarfraz M I, Ghafoor A. Risk-aware management of virtual resources in access controlled service-oriented cloud datacenters. IEEE Transactions on Cloud Computing, 2015, 6(1): 168–181

    Article  Google Scholar 

  43. Xie Y, Wen H, Wu B, Jiang Y, Meng J. A modified hierarchical attribute-based encryption access control method for mobile cloud computing. IEEE Transactions on Cloud Computing, 2019, 7(2): 383–391

    Article  Google Scholar 

  44. Lu M, Lai X, Xiao G, Qin L. Symmetric-key cryptosystem with DNA technology. Science in China Series F: Information Sciences, 2007, 50(3): 324–333

    MATH  Google Scholar 

  45. Murugan A, Thilagavathy R. Cloud storage security scheme using DNA computing with Morse code and zigzag pattern. In: Proceedings of 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI). 2017, 2263–2268

  46. Alam Q, Malik S U R, Akhunzada A, Choo K K R, Tabbasum S, Alam M. A cross tenant access control (CTAC) model for cloud computing: formal specification and verification. IEEE Transactions on Information Forensics and Security, 2017, 12(6): 1259–1268

    Article  Google Scholar 

  47. Wang Y, Han Q, Cui G, Sun J. Hiding messages based on DNA sequence and recombinant DNA technique. IEEE Transactions on Nanotechnology, 2019, 18: 299–307

    Article  Google Scholar 

  48. Enayatifar R, Abdullah A H, Isnin I F. Chaos-based image encryption using a hybrid genetic algorithm and a DNA sequence. Optics and Lasers in Engineering, 2014, 56: 83–93

    Article  Google Scholar 

  49. Alghafis A, Firdousi F, Khan M, Batool S I, Amin M. An efficient image encryption scheme based on chaotic and deoxyribonucleic acid sequencing. Mathematics and Computers in Simulation, 2020, 177: 441–466

    Article  MathSciNet  MATH  Google Scholar 

  50. Elhadad A. Data sharing using proxy re-encryption based on DNA computing. Soft Computing, 2020, 24(3): 2101–2108

    Article  Google Scholar 

  51. Namasudra S. Fast and secure data accessing by using DNA computing for the cloud environment. IEEE Transactions on Services Computing, 2020, 15(4): 2289–2300

    Article  Google Scholar 

  52. Reddy M I, Kumar A P S, Reddy K S. A secured cryptographic system based on DNA and a hybrid key generation approach. Biosystems, 2020, 197: 104207

    Article  Google Scholar 

  53. Liu C, Yang C, Zhang X, Chen J. External integrity verification for outsourced big data in cloud and IoT: a big picture. Future Generation Computer Systems, 2015, 49: 58–67

    Article  Google Scholar 

  54. Elhoseny M, Abdelaziz A, Salama A S, Riad A M, Muhammad K, Sangaiah A K. A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems, 2018, 86: 1383–1394

    Article  Google Scholar 

  55. Wang T, Mei Y, Liu X, Wang J, Dai H N, Wang Z. Edge-based auditing method for data security in resource-constrained internet of things. Journal of Systems Architecture, 2021, 114: 101971

    Article  Google Scholar 

  56. Sarosh P, Parah S A, Bhat G M, Muhammad K. A security management framework for big data in smart healthcare. Big Data Research, 2021, 25: 100225

    Article  Google Scholar 

  57. Yu W, Liu Y, Dillon T, Rahayu W, Mostafa F. An integrated framework for health state monitoring in a smart factory employing IoT and big data techniques. IEEE Internet of Things Journal, 2022, 9(3): 2443–2454

    Article  Google Scholar 

  58. Wang T, Yang Q, Shen X, Gadekallu T R, Wang W, Dev K. A privacy-enhanced retrieval technology for the cloud-assisted internet of things. IEEE Transactions on Industrial Informatics, 2022, 18(7): 4981–4989

    Article  Google Scholar 

  59. Forouzan B A, Mukhopadhyay D. Cryptography and Network Security (Sie). McGraw-Hill Education, 2011

  60. Pavithran P, Mathew S, Namasudra S, Singh A. Enhancing randomness of the ciphertext generated by DNA-based cryptosystem and finite state machine. Cluster Computing, 2022: https://doi.org/10.1007/s10586-022-03653-9

  61. Aieh A, Sen A, Dash S R, Dehuri S. Deoxyribonucleic acid (DNA) for a shared secret key cryptosystem with Diffie Hellman key sharing technique. In: Proceedings of the 3rd International Conference on Computer, Communication, Control and Information Technology (C3IT). 2015, 1–6

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ashish Singh.

Additional information

Ashish Singh is currently working as an Assistant Professor, School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, India. He completed his BE and MTech in Computer Science and Engineering in 2013 and 2015, respectively. The PhD degree has been received in Computer Science & Engineering from the National Institute of Technology Patna (Bihar), under Visvesvaraya PhD Scheme for Electronics & IT Ministry of Electronics & Information Technology (MeitY) Government of India in 2020. His research areas are cloud security, trust management, healthcare security, Internet of Things, access control, edge computing, and network security. He has published articles in different Journals, including Journal of Network and Computer Applications, ICT Express, Journal of Ambient Intelligence and Humanized Computing, Multimedia Tools and Applications, and others. He has also published many conference proceedings in prestigious international conferences.

Abhinav Kumar is currently working as an Assistant Professor at Department of Computer Science and Engineering, Indian Institute of Information Technology Surat, India. He has obtained a PhD degree in Computer Science & Engineering from the Department of Computer Science and Engineering of the National Institute of Technology Patna, India. His research interests include machine learning, deep learning, crisis informatics, natural language processing, and social networks. He has published articles in different Journals, including Applied Soft Computing, Annals of Operation Research, IEEE IT Professional, IEEE Transactions of Industrial Informatics, Sustainable Cities and Society, Information Systems Frontiers, International Journal of Disaster Risk Reduction, and others.

Suyel Namasudra is an assistant professor in the Department of Computer Science and Engineering at the National Institute of Technology Agartala, India. Before joining the National Institute of Technology Agartala, Dr. Namasudra was an assistant professor in the Department of Computer Science and Engineering at the National Institute of Technology Patna, India, and a post-doctorate fellow at the International University of La Rioja (UNIR), Spain. He has received PhD degree in Computer Science and Engineering from the National Institute of Technology Silchar, India. His research interests include blockchain technology, cloud computing, IoT, and DNA computing. Dr. Namasudra has edited 4 books, 5 patents, and 60 publications in conference proceedings, book chapters, and refereed journals like IEEE TII, IEEE T-ITS, IEEE TSC, IEEE TCSS, ACM TOMM, ACM TALLIP, FGCS, CAEE, and many more. He has served as a Lead Guest Editor/Guest Editor in many reputed journals like ACM TOMM (ACM, IF: 3.144), CAEE (Elsevier, IF: 3.818), CAIS (Springer, IF: 4.927), CMC (Tech Science Press, IF: 3.772), Sensors (MDPI, IF: 3.576), and many more. Dr. Namasudra has participated in many international conferences as an Organizer and Session Chair. He is a member of IEEE and ACM. Dr. Namasudra has been featured in the list of top 2% scientists in the world in 2021 and 2022, and his h-index is 25.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, A., Kumar, A. & Namasudra, S. DNACDS: Cloud IoE big data security and accessing scheme based on DNA cryptography. Front. Comput. Sci. 18, 181801 (2024). https://doi.org/10.1007/s11704-022-2193-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-022-2193-3

Keywords

Navigation