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Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

A discrete mathematical framework for security and cryptographic analysis of Ethereum smart contracts

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pp. 2969–2976Vol. 47Issue 8August 2026DOI: 10.47974/JIOS-2238XML
Received:
01 Dec 2025
Published Online:
31 Aug 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2238
Pages:
2969–2976

Abstract

Ethereum smart contracts execute immutable and financially critical logic, making post-deployment security flaws especially expensive to remediate. Existing protection mechanisms typically depend on either static code inspection or dynamic execution-based testing, often lacking formal security assurances. This paper introduces a discrete mathematics based hybrid static and dynamic security and encryption analysis framework for Ethereum smart contracts that is both formally rigorous and practically deployable. Smart contracts are represented as discrete transition systems composed of finite states, executable actions, and security invariants, enabling formal reasoning about contract behavior. Static analysis derives control-flow and state-transition relationships to identify invariant violations before deployment, while dynamic analysis validates runtime execution traces against the discrete mathematical model. The framework additionally incorporates an encryption aware access control mechanism defined through key-dependent discrete transitions. Experimental evaluation on vulnerable Ethereum smart contracts shows enhanced vulnerability detection coverage and a reduction in false positives compared to standalone analysis approaches. The proposed framework provides a scalable, mathematically grounded solution for improving the security, encryption assurance, and reliability of Ethereum smart contracts.

Keywords

Subject Classifications

68M2568N30

References

[1] V. P. Singh, S. S. Biswas, B. Alankar, and S. Tanweer, “Cryptographic modeling and discrete structures for intrusion detection in Ethereum smart contracts,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 8, pp. 3039–3048 (2025).

[2] A. Dadhich, B. Keswani, and D. Goyal, “Comparative analysis of a novel smart contract-based hybrid access control model for blockchain-enabled secure IoT home automation systems,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 7, pp. 2875–2888 (2025).

[3] V. Ramachandran, H. M. A. Ghanimi, B. Marapelli, N. Kaur, M. R. Laxmi, R. K. Bommisetti, S. Sengan, and P. Dadheech, “An enterprise blockchain model: A reliable cryptography-based cyber-physical systems for securing user data,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-B, pp. 2103–2114 (2025).

[4] L. Luu, D.-H. Chu, H. Olickel, P. Saxena, and A. Hobor, “Making smart contracts smarter,” in Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), Vienna, Austria, pp. 254–269 (2016).

[5] J. Feist, G. Grieco, and A. Groce, “Slither: A static analysis framework for smart contracts,” in Proc. IEEE/ACM 2nd Int. Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB), Montreal, QC, Canada, pp. 8–15 (May 2019).

[6] ConsenSys, “Mythril,” GitHub repository. [Online]. Available: https://github.com/ConsenSys/mythril. [Accessed: Nov. 5, 2025]

[7] S. Tikhomirov, E. Voskresenskaya, I. Ivanitskiy, R. Takhaviev, E. Marchenko, and Y. Alexandrov, “SmartCheck: Static analysis of Ethereum smart contracts,” in Proc. 1st Int. Workshop on Emerging Trends in Software Engineering for Blockchain, Gothenburg, Sweden, pp. 9–16 (May 2018).

[8] P. Tsankov, A. Dan, D. Drachsler-Cohen, A. Gervais, F. Bünzli, and M. Vechev, “Securify: Practical security analysis of smart contracts,” in Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), Toronto, ON, Canada, pp. 67–82 (2018).

[9] B. Jiang, Y. Liu, and W. K. Chan, “ContractFuzzer: Fuzzing smart contracts for vulnerability detection,” in Proc. 33rd ACM/IEEE Int. Conf. Automated Software Engineering (ASE), Montpellier, France, pp. 259–269 (Sep. 2018).

[10] G. Grieco, W. Song, A. Cygan, J. Feist, and A. Groce, “Echidna: Effective, usable, and fast fuzzing for smart contracts,” in Proc. 29th ACM SIGSOFT Int. Symp. Software Testing and Analysis (ISSTA), Virtual Event, USA, pp. 557–560 (Jul. 2020), doi: 10.1145/3395363.3404366.

[11] S. Kalra, S. Goel, M. Dhawan, and S. Sharma, “Zeus: Analyzing safety of smart contracts,” in Proc. Network and Distributed System Security Symposium (NDSS), San Diego, CA, USA, pp. 1–12 (Feb. 2018).

[12] I. Nikolić, A. Kolluri, I. Sergey, P. Saxena, and A. Hobor, “Finding the greedy, prodigal, and suicidal contracts at scale,” in Proc. 34th Annual Computer Security Applications Conference (ACSAC), San Juan, PR, USA, pp. 653–663 (Dec. 2018).

[13] SmartBugs Project, “SolidiFI Benchmark,” GitHub repository. [Online]. Available: https://github.com/smartbugs/SolidiFI-benchmark. [Accessed: Nov. 5, 2025].

[14] SmartBugs Project, “SB Curated: A curated dataset of vulnerable Solidity smart contracts,” GitHub repository. [Online]. Available: https://github.com/smartbugs/smartbugs-curated. [Accessed: Nov. 5, 2025].

[15] SmartBugs Project, “SmartBugs Wild Dataset,” GitHub repository. [Online]. Available: https://github.com/smartbugs/smartbugs-wild. [Accessed: Nov. 5, 2025].

[16] Etherscan, “The Ethereum Blockchain Explorer.” [Online]. Available: https://etherscan.io. [Accessed: Nov. 5, 2025)].

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