CICM: A Collaborative Integrity Checking Blockchain Consensus Mechanism for Preserving the Originality of Data the Cloud for Forensic Investigation

Omoniyi Wale Salami; Muhammad Bashir  Abdulrazaq; Emmanuel Adewale Adedokun; Basira Yahaya

doi:10.22219/kinetik.v7i1.1378

Issue

Vol. 7, No. 1, February 2022

Issue Published : Feb 28, 2022

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

CICM: A Collaborative Integrity Checking Blockchain Consensus Mechanism for Preserving the Originality of Data the Cloud for Forensic Investigation

https://doi.org/10.22219/kinetik.v7i1.1378

Omoniyi Wale Salami

Ahmadu Bello University, Zaria, Nigeria

Muhammad Bashir Abdulrazaq

Ahmadu Bello University

Emmanuel Adewale Adedokun

Ahmadu Bello University

Basira Yahaya

Ahmadu Bello University

Corresponding Author(s) : Omoniyi Wale Salami

salamiow@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 7, No. 1, February 2022
Article Published : Feb 28, 2022

Abstract

The originality of data is very important for achieving correct results from forensic analysis of data for resolving the issue. Data may be analysed to resolve disputes or review issues by finding trends in the dataset that can give clues to the cause of the issue. Specially designed foolproof protection for data integrity is required for forensic purposes. Collaborative Integrity Checking Mechanism (CICM), for securing the chain-of-custody of data in a blockchain is proposed in this paper. Existing consensus mechanisms are fault-tolerant, allowing a threshold for faults. CICM avoids faults by using a transparent 100% agreement process for validating the originality of data in a blockchain. A group of agreement actors check and record the original status of data at its time of arrival. Acceptance is based on general agreement by all the participants in the consensus process. The solution was tested against practical byzantine fault tolerant (PBFT), Zyzzyva, and hybrid byzantine fault tolerant (hBFT) mechanisms for efficacy to yield correct results and operational performance costs. Binomial distribution was used to examine the CICM efficacy. CICM recorded zero probability of failure while the benchmarks recorded up to 8.44%. Throughput and latency were used to test its operational performance costs. The hBFT recorded the best performance among the benchmarks. CICM achieved 30.61% higher throughput and 21.47% lower latency than hBFT. In the robustness against faults tests, CICM performed better than hBFT with 16.5% higher throughput and 14.93% lower latency than the hBFT in the worst-case fault scenario.

Keywords

Forensic Investigation Blockchain Consensus Mechanism Agreement Process Data Originality Data Integrity

Salami, O. W., Abdulrazaq, M. B. ., Adedokun, E. A., & Yahaya, B. (2022). CICM: A Collaborative Integrity Checking Blockchain Consensus Mechanism for Preserving the Originality of Data the Cloud for Forensic Investigation. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 7(1), 55-68. https://doi.org/10.22219/kinetik.v7i1.1378

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References

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References

B. Yahaya, M. B. Mu’azu, and S. Garba, “Congestion Control Strategies on Integrated Routing Protocol for the Opportunistic Network: A Comparative Study and Performance Analysis,” Int. J. Comput. Appl., vol. 117, no. 4, pp. 975–8887, 2015.

O. I. Ademu, C. O. Imafidon, and D. S. Preston, “A New Approach of Digital Forensic Model for Digital Forensic Investigation,” Int. J. Adv. Comput. Sci. Appl., vol. 2, no. 12, pp. 175–178, 2011, doi: 10.14569/ijacsa.2011.021226.

O. W. Salami, I. J. Umoh, E. A. Adedokun, and M. B. Muazu, “Implementing Flash Event Discrimination in IP Traceback using Shark Smell Optimisation Algorithm,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 3, pp. 259–268, 2019, doi: 10.22219/kinetik.v4i3.740.

R. F. Erbacher, “Validation for digital forensics,” ITNG2010 - 7th Int. Conf. Inf. Technol. New Gener., pp. 756–761, 2010, doi: 10.1109/ITNG.2010.18.

N. Chaudhry and M. M. Yousaf, “Consensus Algorithms in Blockchain: Comparative Analysis, Challenges and Opportunities,” in ICOSST 2018 - 2018 International Conference on Open Source Systems and Technologies, Proceedings, Jan. 2019, pp. 54–63, doi: 10.1109/ICOSST.2018.8632190.

Q. Wang, J. Huang, S. Wang, Y. Chen, P. Zhang, and L. He, “A Comparative Study of Blockchain Consensus Algorithms,” J. Phys. Conf. Ser., vol. 1437, no. 012007, pp. 1–8, Jan. 2020, doi: 10.1088/1742-6596/1437/1/012007.

S. S. Hazari and Q. H. Mahmoud, “Comparative evaluation of consensus mechanisms in cryptocurrencies,” Internet Technol. Lett., vol. 2, no. 3, pp. 1–6, May 2019, doi: 10.1002/ITL2.100.

J. Mo, Z. Hu, H. Chen, and W. Shen, “An efficient and provably secure anonymous user authentication and key agreement for mobile cloud computing,” Wirel. Commun. Mob. Comput., vol. 2019, no. Article ID 4520685, pp. 1–12, 2019, doi: 10.1155/2019/4520685.

A. R. Ikuesan and H. S. Venter, “Digital forensic readiness framework based on behavioral-biometrics for user attribution,” in 2017 IEEE Conference on Application, Information and Network Security (AINS), 2017, pp. 54–59, doi: 10.1109/AINS.2017.8270424.

S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008. Accessed: Dec. 08, 2019. [Online]. Available: www.cryptovest.co.uk.

A. H. Lone and R. N. Mir, “Forensic-chain: Blockchain based digital forensics chain of custody with PoC in Hyperledger Composer,” Digit. Investig., vol. 28, pp. 44–55, 2019, doi: 10.1016/j.diin.2019.01.002.

K. Christidis and M. Devetsikiotis, “Blockchains and Smart Contracts for the Internet of Things,” IEEE Access, vol. 4. Institute of Electrical and Electronics Engineers Inc., pp. 2292–2303, 2016, doi: 10.1109/ACCESS.2016.2566339.

W. Yan, J. Shen, Z. Cao, and X. Dong, “Blockchain Based Digital Evidence Chain of Custody,” in the 2020 The 2nd International Conference on Blockchain Technology, 2020, pp. 19–23, doi: 10.1145/3390566.3391690.

L. A. Ajao, J. Agajo, E. A. Adedokun, and L. Karngong, “Crypto Hash Algorithm-Based Blockchain Technology for Managing Decentralized Ledger Database in Oil and Gas Industry,” Multidiscip. Sci. J., vol. 2, pp. 300–325, 2019, doi: 10.3390/j2030021.

M. Li et al., “CrowdBC: A Blockchain-based Decentralized Framework for Crowdsourcing,” IEEE Trans. Parallel Distrib. Syst., 2018, doi: 10.1109/TPDS.2018.2881735.

L. S. Sankar, M. Sindhu, and M. Sethumadhavan, “Survey of consensus protocols on blockchain applications,” in 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, Aug. 2017, pp. 1–5, doi: 10.1109/ICACCS.2017.8014672.

L. Lamport and D. Equipment, “The Part-Time Parliament,” ACMTransactionsonComputerSystems, vol. 16, no. 2, pp. 133–169, 1998.

L. Tseng, Q. Zhang, and Y. Zhang, “Brief Announcement: Reaching Approximate Consensus When Everyone May Crash,” in 34th International Symposium on Distributed Computing (DISC 2020), 2020, vol. 53, pp. 53:1–53:3, doi: 10.4230/LIPIcs.DISC.2020.53.

H. Samy, A. Tammam, A. Fahmy, and B. Hasan, “Enhancing the performance of the blockchain consensus algorithm using multithreading technology,” Ain Shams Eng. J., vol. 12, no. 3, pp. 2709–2716, Sep. 2021, doi: 10.1016/J.ASEJ.2021.01.019.

L. Lamport, R. Shostak, and M. Pease, “The Byzantine Generals Problem,” ACM Trans. Program. Lang. Syst., vol. 4, no. 3, pp. 382–401, 1982, doi: 10.1145/357172.357176.

C. M and L. B, “Practical byzantine fault tolerance and proactive recovery[J],” ACM Trans. Comput. Syst., vol. 20, no. 4, pp. 398–461, 2002.

R. Kotla, L. Alvisi, M. Dahlin, A. Clement, and E. Wong, “Zyzzyva: Speculative Byzantine Fault Tolerance,” ACM Trans. Comput. Syst., vol. 27, no. 4, pp. 7:1-7:39, Jan. 2009, doi: 10.1145/1658357.1658358.

S. Duan, S. Peisert, and K. N. Levitt, “Hbft: Speculative Byzantine fault tolerance with minimum cost,” IEEE Trans. Dependable Secur. Comput., vol. 12, no. 1, pp. 58–70, Jan. 2015, doi: 10.1109/TDSC.2014.2312331

D. Mazières, “The Stellar Consensus Protocol : A Federated Model for Internet-level Consensus,” pp. 1–45, 2015.

L. Lamport, “Paxos Made Simple,” ACM SIGACT News, vol. 32, no. 4, pp. 18–25, 2001, doi: 10.1145/954092.954102

D. Ongaro and J. Ousterhout, “In search of an understandable consensus algorithm,” in Proceedings of the 2014 USENIX Annual Technical Conference, USENIX ATC 2014, 2014, pp. 305–319.

S. Chaisawat, C. V.-2020 17th I. Joint, and U. 2020, “Fault-Tolerant Architecture Design for Blockchain-Based Electronics Voting System,” ieeexplore.ieee.org, Accessed: Nov. 26, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9268264/

Issue

Vol. 7, No. 1, February 2022

CICM: A Collaborative Integrity Checking Blockchain Consensus Mechanism for Preserving the Originality of Data the Cloud for Forensic Investigation

Corresponding Author(s) : Omoniyi Wale Salami

Abstract

Keywords

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References

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