SSB: Smart Contract Security Detection Tool Suitable for Industrial Control Scenarios
The results of this study highlight the effectiveness of the proposed semantic security detection framework, SSB, in identifying a wide range of vulnerabilities in smart contracts tailored for industrial control scenarios. Compared to existing tools like ZEUS, Securify, and VULTRON, SSB demonstrates...
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| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4695 |
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| author | Ci Tao Shuai He Xingqiu Shen |
| author_facet | Ci Tao Shuai He Xingqiu Shen |
| author_sort | Ci Tao |
| collection | DOAJ |
| description | The results of this study highlight the effectiveness of the proposed semantic security detection framework, SSB, in identifying a wide range of vulnerabilities in smart contracts tailored for industrial control scenarios. Compared to existing tools like ZEUS, Securify, and VULTRON, SSB demonstrates superior logical coverage across various vulnerability types, as evidenced by its performance on smart contract samples. This suggests that semantic-based approaches, which integrate domain-specific invariants and runtime monitoring, can address the unique challenges of ICS, such as real-time constraints and semantic consistency between code and physical control logic. The framework’s ability to model industrial invariants—covering security, functionality, consistency, time-related, and resource consumption aspects—provides a robust mechanism to prevent critical errors like unauthorized access or premature equipment operation. However, the lack of real-world ICS validation due to confidentiality constraints limits the generalizability of these findings. Future research should focus on adapting SSB for real industrial deployments, exploring scalability across diverse ICS architectures, and integrating advanced AI techniques for dynamic invariant adjustment. Additionally, addressing cross-chain interoperability and privacy concerns could further enhance the framework’s applicability in complex industrial ecosystems. |
| format | Article |
| id | doaj-art-56980e2d0b3d4c8aac9125d25bf53355 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-56980e2d0b3d4c8aac9125d25bf533552025-08-20T03:02:58ZengMDPI AGSensors1424-82202025-07-012515469510.3390/s25154695SSB: Smart Contract Security Detection Tool Suitable for Industrial Control ScenariosCi Tao0Shuai He1Xingqiu Shen2College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200437, ChinaCollege of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200437, ChinaCollege of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200437, ChinaThe results of this study highlight the effectiveness of the proposed semantic security detection framework, SSB, in identifying a wide range of vulnerabilities in smart contracts tailored for industrial control scenarios. Compared to existing tools like ZEUS, Securify, and VULTRON, SSB demonstrates superior logical coverage across various vulnerability types, as evidenced by its performance on smart contract samples. This suggests that semantic-based approaches, which integrate domain-specific invariants and runtime monitoring, can address the unique challenges of ICS, such as real-time constraints and semantic consistency between code and physical control logic. The framework’s ability to model industrial invariants—covering security, functionality, consistency, time-related, and resource consumption aspects—provides a robust mechanism to prevent critical errors like unauthorized access or premature equipment operation. However, the lack of real-world ICS validation due to confidentiality constraints limits the generalizability of these findings. Future research should focus on adapting SSB for real industrial deployments, exploring scalability across diverse ICS architectures, and integrating advanced AI techniques for dynamic invariant adjustment. Additionally, addressing cross-chain interoperability and privacy concerns could further enhance the framework’s applicability in complex industrial ecosystems.https://www.mdpi.com/1424-8220/25/15/4695industrial control systemssmart contractsblockchain securityvulnerability detection |
| spellingShingle | Ci Tao Shuai He Xingqiu Shen SSB: Smart Contract Security Detection Tool Suitable for Industrial Control Scenarios Sensors industrial control systems smart contracts blockchain security vulnerability detection |
| title | SSB: Smart Contract Security Detection Tool Suitable for Industrial Control Scenarios |
| title_full | SSB: Smart Contract Security Detection Tool Suitable for Industrial Control Scenarios |
| title_fullStr | SSB: Smart Contract Security Detection Tool Suitable for Industrial Control Scenarios |
| title_full_unstemmed | SSB: Smart Contract Security Detection Tool Suitable for Industrial Control Scenarios |
| title_short | SSB: Smart Contract Security Detection Tool Suitable for Industrial Control Scenarios |
| title_sort | ssb smart contract security detection tool suitable for industrial control scenarios |
| topic | industrial control systems smart contracts blockchain security vulnerability detection |
| url | https://www.mdpi.com/1424-8220/25/15/4695 |
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