Decentralized Device Authentication for Cloud systems with Blockchain using Skip Graph Algorithm
Cloud computing provides centralized computing services to the user on demand. Despite this sophisticated service, it suffers from single-point failure, which blocks the entire system. Many security operations consider this single-point failure, which demands alternate security solutions to the aforesaid problem. Blockchain technology provides a corrective measure to a single-point failure with the decentralized operation. The devices communicating in the cloud environment range from small IoT devices to large cloud data storage. The nodes should be effectively authenticated in a blockchain environment. Mutual authentication is time-efficient when the network is small. However, as the network scales, authentication is less time-efficient, and dynamic scalability is not possible with smart contract-based authentication. To address this issue, the blockchain node runs the skip graph algorithm to retrieve the registered node. The skip graph algorithm possesses scalability and decentralized nature, and retrieves a node by finding the longest prefix matching. The worst time complexity is O (log n) for maximum n nodes. This method ensures fast nodal retrieval in the mutual authentication process. The proposed search by name id algorithm through skip graph is efficient compared with the state-of-art existing work and the performance is also good compared with the existing work where the latency is reduced by 30–80%, and the power consumption is reduced by 32–50% compared to other considered approaches.
KeywordsAuthentications, block chain, cloud computing, edge computing, fog computing, search by name id algorithm, skip graph,
References1. P. Mell, T. Grance, The NIST definition of cloud computing, National Institute of Standards and Technology Special Publication, NIST Special Publication 800-145, 53: 1–7, 2011.
2. A.T. Velte, T.J. Velte, R. Elsenpeter, Cloud Computing: A Practical Approach, McGraw-Hill, 2011.
3. M. Ahronovitz et al., Cloud Computing Use Cases, A white paper produced by the cloud computing use case discussion group version 4.0, 2010.
4. M. Jensen, J. Schwenk, N. Gruschka, L.L. Iacono, On technical security issues in cloud computing, [in:] 2009 IEEE International Conference on Cloud Computing, 21–25 Sept., Bangalore, India, pp. 109–116, 2009, doi: 10.1109/CLOUD.2009.60.
5. A. Mxoli, M. Gerber, N. Mostert-Phipps, Information security risk measures for cloudbased personal health records, [in:] International Conference on Information Society (i-Society 2014), 1–12 Nov., London, UK, pp. 187–193, 2014, doi: 10.1109/i-Society.2014.7009039.
6. A. Bouayad, A. Blilat, N.E.H. Mejhed, M. El Ghazi, Cloud computing: Security challenges, [in:] 2012 Colloquium in Information Science and Technology, 22–24 Oct., Fez, Morocco, pp. 26–31, 2012, doi: 10.1109/CIST.2012.6388058.
7. B.R. Kandukuri, Ramakrishna Paturi V., A. Rakshit, Cloud security issues, [in:] 2009 IEEE International Conference on Services Computing, 21–25 Sept., Bangalore, India, pp. 517–520, 2009, doi: 10.1109/SCC.2009.84.
8. D. Riquet, G. Grimaud, M. Hauspie, Large-scale coordinated attacks: Impact on the cloud security, [in:] 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 4–6 July, Palermo, Italy, pp. 558–563, 2012, doi: 10.1109/IMIS.2012.76.
9. K. Kourai, T. Azumi, S. Chiba, A self-protection mechanism against stepping-stone attacks for IaaS clouds, [in:] 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing (UIC/ATC), 4–7 Sept., Fukuoka, Japan, pp. 539–546, 2012, doi: 10.1109/UICATC.2012.13.
10. H. Wu, Y. Ding, C. Winer, L. Yao, Network security for virtual machine in cloud computing, [in:] 5th International Conference on Computer Sciences and Convergence Information Technology (ICCIT), 30 Nov. – 2 Dec., Seoul, South Korea, pp. 18–21, 2010, doi: 10.1109/ICCIT.2010.5711022.
11. T. Acar, M. Belenkiy, A. Küpçü, Single password authentication, Computer Networks, 57(13): 2597–2614, 2013, doi: 10.1016/j.comnet.2013.05.007.
12. P. Liu, S.H. Shirazi, W. Liu, Y. Xie, pKAS: A secure password-based key agreement scheme for the edge cloud, Security and Communication Networks, 2021: Article ID 6571700, pp. 1–10, 2021, doi: 10.1155/2021/6571700.
13. S.M. Gurav, L.S. Gawade, P.K. Rane, N.R. Khochare, Graphical password authentication: Cloud securing scheme, [in:] 2014 IEEE International Conference on Electronic Systems, Signal Processing and Computing Technologies, 9–11 Jan., Nagpur, India, pp. 479–483, 2014, doi: 10.1109/ICESC.2014.90.
14. A.A. Yassin, H. Jin, A. Ibrahim, D. Zou, Anonymous password authentication scheme by using digital signature and fingerprint in cloud computing, [in:] 2012 Second IEEE International Conference on Cloud and Green Computing, 1–3 Nov., Xiangtan, China, pp. 282–289, 2012, doi: 10.1109/CGC.2012.91.
15. M. Karnan, M. Akila, N. Krishnaraj, Biometric personal authentication using keystroke dynamics: A review, Applied Soft Computing, 11(2): 1565–1573, 2011, doi: 10.1016/j.asoc.2010.08.003.
16. K. Abhishek, S. Roshan, P. Kumar, R. Ranjan, A comprehensive study on multifactor authentication schemes, [in:] N. Meghanathan, D. Nagamalai, N. Chaki [Eds.], Advances in Computing and Information Technology, Advances in Intelligent Systems and Computing, 177: 561–568, Springer, Berlin, Heidelberg, 2013, doi: 10.1007/978-3-642-31552-7_57.
17. E.T. Anzaku, H. Sohn, Y.M. Ro, Multi-factor authentication using fingerprints and userspecific random projection, [in:] IEEE 2010 12th International Asia-Pacific Web Conference, 6–8 April, Busan, South Korea, pp. 415–418, 2010, doi: 10.1109/APWeb.2010.44.
18. S. Ziyad, A. Kannammal, A multifactor biometric authentication for the cloud, [in:] G. Krishnan, R. Anitha, R. Lekshmi, M. Kumar, A. Bonato, M. Graña [Eds.], Computational Intelligence, Cyber Security and Computational Models, 246: 395–403, Springer, New Delhi, 2014, doi: 10.1007/978-81-322-1680-3_43.
19. X.C. Jiang, J.D. Zheng, An indirect fingerprint authentication scheme in cloud computing, Applied Mechanics and Materials, 484–485: 986–990, 2014, doi: 10.4028/www.scientific.net/AMM.484-485.986.
20. M. Babaeizadeh, M. Bakhtiari, M.A. Maarof, Keystroke dynamic authentication in mobile cloud computing, International Journal of Computer Applications, 90(1): 29–36, 2014, doi: 10.5120/15541-4274.
21. M.A. Ferrer, A. Morales, C.M. Travieso, J.B. Alonso, Low cost multimodal biometric identification system based on hand geometry, palm and finger print texture, [in:] 2007 41st IEEE International Carnahan Conference on Security Technology, 8–11 Oct., Ottawa, Canada, pp. 52–58, 2007, doi: 10.1109/CCST.2007.4373467.
22. B. Cui, T. Xue, Design and realization of an intelligent access control system based on voice recognition, [in:] 2009 ISECS International Colloquium on Computing, Communication, Control, and Management, 8–9 Aug., Sanya, China, pp. 229–232, 2009, doi: 10.1109/CCCM.2009.5270462.
23. R. Jafri, H.R. Arabnia, A survey of face recognition techniques, Journal of Information Processing Systems, 5(2): 41–68, 2009, doi: 10.3745/JIPS.2009.5.2.041.
24. A.K. Jain, S. Prabhakar, L. Hong, S. Pankanti, Filterbank-based fingerprint matching, IEEE Transactions on Image Processing, 9(5): 846–859, 2000, doi: 10.1109/83.841531.
25. D. Zissis, D. Lekkas, Addressing cloud computing security issues, Future Generation Computer Systems, 28(3): 583–592, 2012, doi: 10.1016/j.future.2010.12.006.
26. J. Chen, G. Wu, L. Shen, Z. Ji, Differentiated security levels for personal identifiable information in identity management system, Expert Systems with Applications, 38(11): 14156–14162, 2011, doi: 10.1016/j.eswa.2011.04.226.
27. U. Khalid, M. Asim, T. Baker, P.C.K. Hung, M.A. Tariq, L. Rafferty, A decentralized lightweight blockchain-based authentication mechanism for IoT systems, Cluster Computing, 23: 2067–2087, 2020, doi: 10.1007/s10586-020-03058-6.
28. M.T. Hammi, B. Hammi, P. Bellot, A. Serhrouchni, Bubbles of Trust: A decentralized blockchain-based authentication system for IoT, Computers & Security, 78: 126–142, 2018, doi: 10.1016/j.cose.2018.06.004.
29. C.H. Lau, K.-H.Y. Alan, F. Yan, Blockchain-based authentication in IoT networks, [in:] 2018 IEEE Conference on Dependable and Secure Computing (DSC), 10–13 Dec., Kaohsiung, Taiwan, pp. 1–8, 2018, doi: 10.1109/DESEC.2018.8625141.
30. D. Li, W. Peng, W. Deng, F. Gai, A blockchain-based authentication and security mechanism for IoT, [in:] 2018 27th IEEE International Conference on Computer Communication and Networks (ICCCN), 30 July – 2 Aug., Hangzhou, China, pp. 1–6, 2018, doi: 10.1109/ICCCN.2018.8487449.
31. G. Kumar, R. Saha, M.K. Rai, R. Thomas, T.H. Kim, Proof-of-work consensus approach in blockchain technology for cloud and fog computing using maximization-factorization statistics, IEEE Internet of Things Journal, 6(4): 6835–6842, 2019, doi: 10.1109/JIOT.2019.2911969.
32. J. Kang, Z. Xiong, D. Niyato, P. Wang, D. Ye, D.I. Kim, Incentivizing consensus propagation in proof-of-stake based consortium blockchain networks, [in:] IEEE Wireless Communications Letters, 8(1): 157–160, 2019, doi: 10.1109/LWC.2018.2864758.
33. J. Sousa, A. Bessani, M. Vukolic, A Byzantine fault-tolerant ordering service for the hyperledger fabric blockchain platform, [in:] 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 25–28 June, Luxembourg, Luxembourg, pp. 51–58, 2018, doi: 10.1109/DSN.2018.00018.
34. B. Chase, E. MacBrough, Analysis of the XRP ledger consensus protocol, 2018, doi: 10.48550/arXiv.1802.07242.
35. Y. Hassanzadeh-Nazarabadi, A.U. Sahin, Ö. Özkasap, A. Küpçü, SkipSim: Scalable skip graph simulator, [in:] 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2–6 May, Toronto, Canada, pp. 1–2, 2020, doi: 10.1109/ICBC48266.2020.9169426.
36. F. Wu, X. Li, L. Xu, S. Kumari, M. Karuppiah, J. Shen, A lightweight and privacy-preserving mutual authentication scheme for wearable devices assisted by cloud server, Computers & Electrical Engineering, 63: 168–181, 2017, doi: 10.1016/j.compeleceng.2017.04.012.
37. M.N. Aman, K.C. Chua, B. Sikdar, Mutual authentication in IoT systems using physical unclonable functions, IEEE Internet of Things Journal, 4(5): 1327–1340, 2020, doi: 10.1109/JIOT.2017.2703088.
38. P. Gope, B. Sikdar, Lightweight and privacy-preserving two-factor authentication scheme for IoT devices, IEEE Internet of Things Journal, 6(1): 580–589, 2019, doi: 10.1109/JIOT.2018.2846299.
39. A. Singh, K. Chatterjee, A secure multi-tier authentication scheme in cloud computing environment, [in:] 2015 International Conference on Circuits, Power and Computing Technologies, 19–20 March, Nagercoil, India, pp. 1–7, 2015, doi: 10.1109/ICCPCT.2015.7159276.
40. S.M. Bellovin, M. Merritt, Encrypted key exchange: password based protocols secure against dictionary attacks, [in:] Proceedings of 1992 IEEE Computer Society Symposium on Research in Security and Privacy (SRSP92), 4–6 May, Oakland, California, pp. 72–84, 1992, doi: 10.1109/RISP.1992.213269.
41. P.S. Kumar, R. Subramanian, An efficient and secure protocol for ensuring data storage security in cloud computing, IJCSI International Journal of Computer Science Issues, 8(6): 261–274, 2011.
42. K. Gunjan, G. Sahoo, R.K. Tiwari, Identity management in cloud computing – A Review, International Journal of Engineering Research & Technology, 1(4): 1–5, 2012.
43. K. Alhamazani et al., An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art, Computing, 97(4): 357–377, 2015, doi: 10.1007/s00607-014-0398-5.
44. C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel, M. Rajarajan, A survey of intrusion detection techniques in cloud, Journal of Network and Computer Applications, 36(1): 42–57, 2013, doi: 10.1016/j.jnca.2012.05.003.
45. J. Tong, G. Xiong, Y. Zhao, L. Guo, A research on the vulnerability in popular P2P protocols, [in:] 2013 8th International Conference on Communications and Networking in China (CHINACOM), 14–16 Aug., Guilin, China, pp. 405–409, 2013, doi: 10.1109/ChinaCom.2013.6694630.
46. K. Amit, C. Chinmay, J. Wilson, A novel fog computing approach for minimization of latency in healthcare using machine learning, International Journal of Interactive Multimedia and Artificial Intelligence, 6(7): 7–17, 2020, doi: 10.9781/ijimai.2020.12.004.