Securing Financial Systems with Block chain: A Comprehensive Review of Block chain and Cybersecurity Practices
DOI:
10.47709/ijmdsa.v3i4.5104Keywords:
Key words: Block chain antifraud systems, finance solutions, quantum strike, smart contracts, phishing, privacy problematics, quantum techniques, decentralized finances (DeFi), cryptographic algorithms, multi-signature acknowledgments, audits, identity management, compliance, KYC, AML, access rights, scalability, constant monitoring.Dimension Badge Record
Abstract
New and innovative block chain technology is becoming the key in enhancing the security, transparency and efficiency in the financial sector. However, as financial applications based on the block chain expand and improve, so do the block chain threats and safety concerns. This paper aims to discuss the aspects of the block chain security with reference to the financial system and its advantages and drawbacks. It embraces major risks like 51% attacks, smart contract exploits, phishing, and data privacy and security issues; new risks from quantum computing and Decentralized finance (DeFi) platforms. Best practices also outlined in the paper include the use of an industry-grade cryptographic algorithm, a robust multi-signature authentication technique, auditing of the block chain application at regular intervals, the adoption of secure, decentralized identity verification and management, as well as compliance with industry standards such as KYC and AML. It also underlines the need to establish effective access controls and to develop capability in scaling solutions and sustained monitoring. Lastly, it can be noted that the acquisition of block chain-based financial applications entails the use of a combination of measures to address existing and future risks. As these best practices are implemented and the threats advance, the financial institutions will be better placed to realize the full value of block chain technology while at the same time protecting the privacy and security of people’s financial transactions.
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References
Azaria, A. Ekblaw, T. Vieira, and A. Lippman, ‘‘MedRec: Using blockchain for medical data access and permission management,’’ in Proc. 2nd Int. Conf. Open Big Data (OBD), Vienna, Austria, Aug. 2016, pp. 25–30.
J. Zhang, N. Xue, and X. Huang, ‘‘A secure system for pervasive social network-based healthcare,’’ IEEE Access, vol. 4, pp. 9239–9250, 2016.
H. Zhao, Y. Zhang, Y. Peng, and R. Xu, ‘‘Lightweight backup and efficient recovery scheme for health blockchain keys,’’ in Proc. IEEE ISADS, Bangkok, Thailand, Mar. 2017, pp. 229–234.
M. A. Salahuddin, A. Al-Fuqaha, M. Guizani, K. Shuaib, and F. Sallabi, ‘‘Softwarization of Internet of Things infrastructure for secure and smart healthcare,’’ Computer, vol. 50, no. 7, pp. 74–79, 2017.
Q. I. Xia, E. B. Sifah, K. O. Asamoah, J. Gao, X. Du, and M. Guizani, ‘‘MeDShare: Trust-less medical data sharing among cloud service providers via blockchain,’’ IEEE Access, vol. 5, pp. 14757–14767, 2017.
F. Tian, ‘‘A supply chain traceability system for food safety based on HACCP, blockchain & Internet of Things,’’ in Proc. Int. Conf. Service Syst. Service Manage., Dalian, China, Jun. 2017, pp. 1–6.
M. Samaniego and R. Deters, ‘‘Internet of smart things—IoST: Using blockchain and CLIPS to make things autonomous,’’ in Proc. IEEE Int. Conf. Cogn. Comput. (ICCC), Honolulu, HI, USA, Jun. 2017, pp. 9–16.
N. Abbas, Y. Zhang, A. Taherkordi, and T. Skeie, ‘‘Mobile edge computing: A survey,’’ IEEE Internet Things J., vol. 5, no. 1, pp. 450–465, Feb. 2018.
M. S. Hossain, G. Muhammad, and S. U. Amin, ‘‘Improving consumer satisfaction in smart cities using edge computing and caching: A case study of date fruits classification,’’ Future Gener. Comput. Syst., vol. 88, pp. 333–341, Nov. 2018.
Weber et al., ‘‘on availability for blockchain-based systems,’’ in Proc. IEEE 36th Symp. Reliable Distrib. Syst. (SRDS), Hong Kong, Sep. 2017, pp. 64–73.
R. Rivera, J. G. Robledo, V. M. Larios, and J. M. Avalos, ‘‘How digital identity on blockchain can contribute in a smart city environment,’’ in Proc. Int. Smart Cities Conf. (ISC2), Wuxi, China, Sep. 2017, pp. 1–4.
N. Rifi, E. Rachkidi, N. Agoulmine, and N. C. Taher, ‘‘towards using blockchain technology for eHealth data access management,’’ in Proc. 4th Int. Conf. Adv. Biomed. Eng. (ICABME), Beirut, Lebanon, Oct. 2017, pp. 1–4.
K. Yeow, A. Gani, R. W. Ahmad, J. J. P. C. Rodrigues, and K. Ko, ‘‘Decentralized consensus for edge-centric Internet of Things: A review, taxonomy, and research issues,’’ IEEE Access, vol. 6, pp. 1513–1524, 2018.
L. Liu, Z. Chang, X. Guo, S. Mao, and T. Ristaniemi, ‘‘Multiobjective optimization for computation offloading in fog computing,’’ IEEE Internet Things J., vol. 5, no. 1, pp. 283–294, Feb. 2018.
P. Zhang, M. A. Walker, J. White, D. C. Schmidt, and G. Lenz, ‘‘Metrics for assessing blockchain-based healthcare decentralized apps,’’ in Proc. IEEE Healthcom, Dalian, China, Oct. 2017, pp. 1–4.
W. Liu, S. S. Zhu, T. Mundie, and U. Krieger, ‘‘Advanced block-chain architecture for e-health systems,’’ in Proc. IEEE Healthcom, Dalian, China, Oct. 2017, pp. 1–6.
R. Guo, H. Shi, Q. Zhao, and D. Zheng, ‘‘Secure attribute-based signature scheme with multiple authorities for blockchain in electronic health records systems,’’ IEEE Access, vol. 6, pp. 11676–11686, 2018.
X. Liang, J. Zhao, S. Shetty, J. Liu, and D. Li, ‘‘Integrating blockchain for data sharing and collaboration in mobile healthcare applications,’’ in Proc. IEEE PIMRC, Montreal, QC, Canada, Oct. 2017, pp. 1–5.
Esposito, A. De Santis, G. Tortora, H. Chang, and K.-K. R. Choo, ‘‘Blockchain: A panacea for healthcare cloud-based data security and privacy?’’ IEEE Cloud Comput., vol. 5, no. 1, pp. 31–37, Jan. 2018
Ahmad, T. Kumar, M. Liyanage, J. Okwuibe, M. Ylianttila, and A. Gurtov, ‘‘Overview of 5G Security Challenges and Solutions,’’ IEEE Commun. Standards Mag., vol. 2, no. 1, pp. 36–43, Mar. 2018.
T. M. Fernández-Caramés and P. Fraga-Lamas, ‘‘a review on the use of blockchain for the Internet of Things,’’ IEEE Access, vol. 6, pp. 32979–33001, 2018, doi: 10.1109/ACCESS.2018.2842685
L. Ma, X. Liu, Q. Pei, and Y. Xiang, ‘‘Privacy-preserving reputation management for edge computing enhanced mobile crowdsensing,’’ IEEE Trans. Services Comput., to be published, doi: 10.1109/TSC.2018. 2825986.
T. Zhang, ‘‘Data offloading in mobile edge computing: A coalition and pricing based approach,’’ IEEE Access, vol. 6, pp. 2760–2767, 2018
F. Wang, J. Xu, X. Wang, and S. Cui, ‘‘Joint offloading and computing optimization in wireless powered mobile-edge computing systems,’’ IEEE Trans. Wireless Commun., vol. 17, no. 3, pp. 1784–1797, Mar. 2018.
M. Liu, Y. Mao, S. Leng, and S. Mao, ‘‘Full-duplex aided user virtualization for mobile edge computing in 5G networks,’’ IEEE Access, vol. 6, pp. 2996–3007, 2018.
W. Fan, Y. Liu, B. Tang, F. Wu, and Z. Wang, ‘‘Computation offloading based on cooperations of mobile edge computing-enabled base stations,’’ IEEE Access, vol. 6, pp. 22622–22633, 2018
Z. Zhu et al., ‘‘Fair resource allocation for system throughput maximization in mobile edge computing,’’ IEEE Access, vol. 6, pp. 5332–5340, 2018
P. Bellavista, S. Chessa, L. Foschini, L. Gioia, and M. Girolami, ‘‘Humanenabled edge computing: Exploiting the crowd as a dynamic extension of mobile edge computing,’’ IEEE Commun. Mag., vol. 56, no. 1, pp. 145–155, Jan. 2018
Kiani and N. Ansari, ‘‘Edge computing aware NOMA for 5G networks,’’ IEEE Internet Things J., vol. 5, no. 2, pp. 1299–1306, Apr. 2018.
L. Tang and S. He, ‘‘Multi-user computation offloading in mobile edge computing: A behavioral perspective,’’ IEEE Netw., vol. 32, no. 1, pp. 48–53, Jan./Feb. 2018.
Q. Yuan, H. Zhou, J. Li, Z. Liu, F. Yang, and X. S. Shen, ‘‘Toward efficient content delivery for automated driving services: An edge computing solution,’’ IEEE Netw., vol. 32, no. 1, pp. 80–86, Jan./Feb. 2018
M. Marjanovi?, A. Antoni?, and I. P. Žarko, ‘‘Edge computing architecture for mobile crowdsensing,’’ IEEE Access, vol. 6, pp. 10662–10674, 2018.
Y. Hao, M. Chen, L. Hu, M. S. Hossain, and A. Ghoneim, ‘‘Energy efficient task caching and offloading for mobile edge computing,’’ IEEE Access, vol. 6, pp. 11365–11373, 2018.
P. Verma and S. K. Sood, ‘‘Fog assisted-IoT enabled patient health monitoring in smart homes,’’ IEEE Internet Things J., vol. 5, no. 3, pp. 1789–1796, Jun. 2018.
G. Premsankar, M. Di Francesco, and T. Taleb, ‘‘Edge computing for the Internet of Things: A case study,’’ IEEE Internet Things J., vol. 5, no. 2, pp. 1275–1284, Apr. 2018.
Y. Cao et al., ‘‘Mobile edge computing for big-data-enabled electric vehicle charging,’’ IEEE Commun. Mag., vol. 56, no. 3, pp. 150–156, Mar. 2018.
L. Liu, Z. Chang, and X. Guo, ‘‘Socially aware dynamic computation offloading scheme for fog computing system with energy harvesting devices,’’ IEEE Internet Things J., vol. 5, no. 3, pp. 1869–1879, Jun. 2018.
Z. Ali, M. S. Hossain, G. Muhammad, I. Ullah, H. Abachi, and A. Alamri, ‘‘Edge-centric multimodal authentication system using encrypted biometric templates,’’ Future Gener. Comput. Syst., vol. 85, pp. 76–87, Aug. 2018.
H. Ko, J. Lee, and S. Pack, ‘‘Spatial and temporal computation offloading decision algorithm in edge cloud-enabled heterogeneous networks,’’ IEEE Access, vol. 6, pp. 18920–18932, 2018.
K. Zhang, S. Leng, Y. He, S. Maharjan, and Y. Zhang, ‘‘Mobile edge computing and networking for green and low-latency Internet of Things,’’ IEEE Commun. Mag., vol. 56, no. 5, pp. 39–45, May 2018.
Y. Bi, G. Han, C. Lin, Q. Deng, L. Guo, and F. Li, ‘‘Mobility support for fog computing: An SDN approach,’’ IEEE Commun. Mag., vol. 56, no. 5, pp. 53–59, May 2018.
Poon J, Dryja T. 2016. The bitcoin lightning network: Scalable off-chain instant payments. Available at https://lightning.network/lightning-network-paper.pdf (accessed on 1 October 2019).
Popov S. 2018. Seebacher S, Schüritz R. 2017. Blockchain technology as an enabler of service systems: a structured literature review. In: Proceedings of the international conference on exploring services science. 12–23.
ShipChain. 2019. The end–to–end logistics platform of the future: trustless, transparent tracking. Available at https:// shipchain.io/ (accessed on 1 October 2019).
Spagnuolo M, Maggi F, Zanero S. 2014. Bitiodine: extracting intelligence from the bitcoin network. In: Proceedings of the international conference on financial cryptography and data security. 457–468.
Sun Y, Zhang R, Wang X, GAO K, Liu L. 2018. A decentralizing attribute-based signature for healthcare blockchain. In: 2018 27th International conference on computer communication and networks (ICCCN). IEEE, 1–9.
Swan M. 2015. Blockchain: blueprint for a new economy. California, CA, USA: O’Reilly Media. Tanwar S, Parekh K, Evans R. 2020. Blockchain-based electronic healthcare record system for healthcare 4.0 applications. Journal of Information Security and Applications 50:1–13 DOI 10.1016/j.jisa.2019.102407.
TomoChain. 2018. Tomochain: masternodes design technical white paper version 1.0. Available at https://tomochain.com/docs/technical-whitepaper--1.0.pdf (accessed on 1 October 2019). T°, T. 2017. Preventing fake degrees.
Nhân Dân. Available at http:// nhandan.com.vn/ giaoduc/tin-tuc/item/ 33377202-ngan-chan-nan-bang-gia-va-tinh-trang-chay-bangcap.html (accessed on 1 October 2019).
Turkanovi? M, Hölbl M, Koši? K, Heri?ko M, Kamišali? A. 2018. EduCTX: a blockchain-based higher education credit platform. IEEE Access 6:5112–5127 DOI 10.1109/ACCESS.2018.2789929.
Wang H, Cen Y, Li X. 2017. Blockchain router: a cross-chain communication protocol. In: Proceedings of the 6th international conference on informatics, environment, energy and applications. 94–97.
Wang L, Liu Y. 2015. Exploring miner evolution in bitcoin network. In: Proceedings of the international conference on passive and active network measurement. 290–302.
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