Securing Secrets in Cyber-Physical Systems: A Cutting-Edge Privacy Approach with Consortium Blockchain
<p>Proposed framework.</p> "> Figure 2
<p>Proposed system architecture.</p> "> Figure 3
<p>Analysis based on the amount of mean processing time and blocks used in a simulation.</p> "> Figure 4
<p>Simulation results based on the number of transactions and transaction generation time in seconds.</p> "> Figure 5
<p>Analysis based on the number of records and the transaction times.</p> "> Figure 6
<p>Simulation results based on the execution times in seconds and the number of transactions.</p> "> Figure 7
<p>Comparative analysis based on the number of transactions and the storage consmuption in comparison with the benchmark model.</p> "> Figure 8
<p>Simulation results based on the number of transactions and memory consumption.</p> "> Figure 9
<p>Simulation results based on the number of transactions and the Mean execuition Time.</p> ">
Abstract
:1. Introduction
2. Motivation
2.1. Related Work
2.2. Main Contribution
- Proof of improved participant (PoP) consensus algorithm: This research presents a new consensus algorithm tailored to blockchain networks; it is called proof of improved participant (PoP). To guarantee the trustworthiness of the blockchain network, the PoP algorithm checks blocks for validity [20].
- Model for identifying honest miners: This paper introduces a scheme for determining which miners can be trusted and how to prevent malevolent ones from taking part. The suggested model improves the blockchain network’s security and trustworthiness by including methods to detect and prevent harmful activity.
- Integration of proposed consensus algorithm: Integrating the suggested PoP consensus algorithm into the Ethereum framework is made possible by this study in a thorough and workable manner. There will be new opportunities for improved performance and scalability thanks to the integration’s simple installation and interoperability with existing blockchain infrastructure.Ethereum, as per our most recent update in September 2021, is a blockchain platform optimized for DApps and smart contracts. Although Ethereum was not designed with cyber-physical systems in mind, it can be leveraged to create solutions that communicate with CPS hardware. Ethereum would function as the blockchain architecture in this case, allowing for decentralized control and safe data sharing in CPS settings.Here is a conceptual overview of how Ethereum could be utilized within a cyber-physical system:
- Smart contracts: Smart contracts can be programmed on Ethereum and then run autonomously on the blockchain. To guarantee seamless interactions and data exchanges across various CPS components, smart contracts can be programmed to automate and enforce the rules and agreements between them.
- Data integrity: By recording data or sensor readings on the Ethereum blockchain through smart contracts, CPS components can securely and immutably log their data. This ensures the integrity of the data collected from various physical devices and prevents tampering.
- Decentralized control: Ethereum’s decentralized nature allows CPSs to operate without relying on a single central authority. Smart contracts can facilitate the interactions between different components, enabling a distributed control system.
- Transactions and payments: Ethereum’s native cryptocurrency, Ether (ETH), can be used to facilitate transactions and payments within CPS networks. This could enable machines or devices to autonomously pay for services, resources, or maintenance on the network.
- Oracles: For CPSs to interact with the external world, they might require data from off-chain sources (e.g., weather data, financial information). Oracles are mechanisms that allow smart contracts to access external data securely and incorporate it into the blockchain-based operations.
- Interoperability: Ethereum’s widespread adoption and developer community provide opportunities for integration with other blockchain networks or protocols, enabling interoperability between CPSs running on different blockchain platforms.
It is essential to consider the limitations of the Ethereum blockchain, such as scalability and transaction costs, when designing solutions for large-scale CPS applications. Some use cases might require more scalable and specialized blockchain solutions, which are actively being explored and developed within the blockchain space.When applied to the suggested CPS paradigm, Ethereum’s capabilities and flexibility yield substantial improvements in the areas of security, transparency, and decentralized control in a wide range of cyber-physical systems. - Comparative analysis of consensus protocols: In this study, the existing consensus approaches are compared to the proposed methodology for achieving consensus on Proof-of-Participation. This analysis shows the pros and downsides of different consensus algorithms, providing a comprehensive understanding of their capabilities in terms of speed, safety, and scalability.
2.3. Problem Statement
2.4. Preliminaries
2.5. Proposed Proof of Work (PoW)
- Define the requirements and scope of the system: This involves identifying the specific data that need to be stored, potential threats to the data, desired levels of security and privacy, and performance requirements.
- Choose a suitable blockchain platform: The success of the PoW-based healthcare system depends on the blockchain platform chosen. When comparing systems, safety, scalability, and compatibility should all be taken into account.
- Design the blockchain network architecture: The network architecture should be designed to ensure data integrity, transparency, and security. This includes determining the number of nodes, selecting the consensus mechanism, defining the transaction validation process, and establishing the data encryption method.
- Develop the smart contract: A blockchain-based application that can carry out its own instructions is called a smart contract. It can be used to make sure the EHR is storing correct information by automating the data validation process.
- Implement the PoW algorithm: To generate a new block for the blockchain, nodes in the network must solve a difficult mathematical puzzle using the PoW algorithm. The data in the EHR is protected and cannot be altered thanks to this procedure.
- Test and evaluate the system: The PoW-based healthcare system should be thoroughly tested and evaluated to ensure its feasibility and effectiveness. Performance metrics such as transaction throughput, data integrity, and security should be assessed.
2.6. Proof of Stake
- Security: PoS can provide enhanced security compared to PoW. In a PoW network, a 51% attack is possible if a single entity controls more than 51% of the network’s computing power. However, in a PoS network, an attacker would need to control 51% of the total cryptocurrency supply, which is much more challenging to achieve. This makes the PoS networks more resistant to attacks [28].
- Accessibility: PoS is more accessible to individual users as it does not require expensive hardware and high electricity bills associated with PoW mining. Users can participate in the PoS consensus by holding and staking their cryptocurrency, increasing inclusivity in the network [29].
3. Proposed Framework
3.1. System Architecture
- Cyber-physical systems (CPSs): This component represents the physical devices and systems that are interconnected with the digital world. CPSs include sensors, actuators, controllers, and other devices that collect and process data.
- Consortium blockchain: The architecture utilizes a consortium blockchain as the underlying technology for privacy and security. A consortium blockchain is a permissioned blockchain where multiple pre-selected organizations or entities have control over the consensus process [34].
- Secret management: This component is responsible for managing and safeguarding the secrets in the CPSs. Secrets can include sensitive data, cryptographic keys, access credentials, or any other confidential information. The secret management system ensures that secrets are securely stored, accessed, and shared only with authorized entities [35].
- Privacy-enhancing techniques: The architecture incorporates various privacy-enhancing techniques to protect the confidentiality and integrity of secrets. These techniques may include the encryption, secure multi-party computation, zero-knowledge proofs, and differential privacy mechanisms [36].
- Access control: Access control mechanisms are implemented to regulate and enforce the permissions and privileges for accessing secrets. Only authorized entities or participants within the consortium blockchain are granted access to specific secrets based on predefined policies [37].
- Consensus mechanism: To ensure the security and veracity of transactions involving confidential information, the consortium blockchain uses a consensus mechanism to reach an agreement among all parties involved. The blockchain’s immutability and trustworthiness are guaranteed by the consensus process, which assures that all participants agree on the blockchain’s state [38].
- The deployment of smart contracts on the blockchain allows for the automatic implementation of predetermined business logic and regulations. Smart contracts can be used to automate private key administration, access control policies, and confidentiality-preserving tasks within the framework of the architecture [39,40,41,42,43].
- Data exchange and integration: The architecture facilitates secure and private data exchange and integration between the different components of the CPSs. This includes data transmission between sensors, actuators, controllers, and other devices, while ensuring confidentiality and integrity [44,45].
- Audit and compliance: The system architecture includes mechanisms for auditing and compliance to ensure that the privacy design and security measures are adhered to. Compliance with regulatory requirements and standards can be monitored and verified using auditing mechanisms built into the architecture [46].
3.2. Proposed Methodology
- Problem identification: The initial step is to identify the specific problem that the cyber-physical system aims to solve. In this case, our objective is to enhance the security, privacy, and interoperability of healthcare data.
- Requirement definition: Once the problem is identified, the subsequent step is to define the precise requirements for the cyber-physical system. This entails specifying the system’s functionality, performance, security, and scalability requirements.
- Stakeholder identification: Identify the stakeholders who will be utilizing and interacting with the system. This includes healthcare providers, patients, insurance companies, regulators, and other relevant parties.
- Use case definition: Based on the identified requirements and stakeholders, define the specific use cases that the cyber-physical system will address. This involves delineating the particular actions to be undertaken by each stakeholder within the system.
- Smart contract definition: Define the smart contracts that will execute the actions specified in the use cases. SC are self-executing agreements with the contractual terms directly written as code.
- Data structure definition: Specify the data structure that will be employed to store healthcare data on the blockchain. This includes defining the data fields, data types, and encryption mechanisms to be utilized.
- Consensus mechanism definition: Determine the consensus mechanism to validate transactions on the blockchain. In this case, a proof of stake (PoS) consensus mechanism may be suitable due to its energy efficiency and lower computational requirements compared to the proof of work (PoW).
- System testing and deployment: Once the design phase is completed, thoroughly test and deploy the system in a controlled environment. This includes evaluating the system’s functionality, security, and scalability.
- System monitoring and maintenance: After deployment, it is vital to continuously monitor and maintain the system. This includes conducting regular security assessments, addressing potential breaches, and ensuring that the system consistently meets the stakeholders’ specific requirements.
3.3. The Simple, Sessions, and Cookie Protocol Models
3.4. Proposed Protocol
3.5. Miner Selection
- Rule 1: The suggested PoIP uses a mining technique that is distinct from that used by conventional blockchains. The dynamic, non-static difficulty experienced by different users has an effect on PoIP mining. The string concatenate operator is followed by the data for a new block in the encoding. Mining becomes increasingly difficult as the end value decreases.
- Rule 2: New-block developers are responsible for their own expenses as well. The developer’s charge for constructing a new block remains the same even though the resulting income is higher.
- Rule 3: Block developers must have R > , where R is calculated using (4). The value of R can vary based on the participation claim. A higher value in Equation (4) enhances the competitors’ power, while a lower value reduces the security of the blockchain. The recommended value for is 0.50.
Algorithm 1 Proposed algorithm for a cyber-physical system using a blockchain-based healthcare system. |
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4. Mathematical Model
Proposed Algorithm
Algorithm 2 Security algorithm for shielding secrets in cyber-physical systems with consortium blockchain. |
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Algorithm 3 Transaction creation and encryption algorithm. |
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5. Mathematical Model for Threat Detection Using Challenger and Attacker Game
5.1. Definitions
5.2. Variables
5.3. Game Model
- Challenger: Represents the system defender, responsible for detecting threats.
- Attacker: Represents the adversary, attempting to exploit system vulnerabilities.
- Vulnerability selection: The challenger selects a vulnerability to be tested.
- Attacker strategy: The attacker selects a strategy to exploit the selected vulnerability.
- System component selection: The challenger selects a system component to be protected [51].
- Detection technique selection: The challenger selects a detection technique to detect attacks on the chosen component.
- Threat scenario generation: The challenger generates a threat scenario representing the interaction between the attacker, selected vulnerability, protected component, and detection technique.
- Detection outcome: The detection technique evaluates the threat scenario and provides a detection outcome, indicating whether the attack was detected or not.
5.4. Simulations Setup
- Simulation setup: we defined the workload by specifying the number of transactions to be processed. Moreover, the memory allocation was determined and the usage metrics was measured, such as memory consumption per transaction or total memory consumed by the system [53].
- Execute simulations: We repeat the simulation with different quantities of transactions, starting with a low load and increasing it over time. In addition, memory consumption is monitored and logged at predetermined intervals or immediately following each completed transaction. We also made a note of how long each transaction took for later examination.
- System model: The cyber-physical system (CPS) under consideration was modeled, including the various entities such as sensors, actuators, controllers, and communication channels. The interactions and dependencies among these entities were defined.
- Attack scenarios: Threats to the CPS’s security were simulated using a variety of attack scenarios. Denial-of-service (DoS) assaults, tampering with data, and unauthorised access were all part of these hypothetical situations. The scope and intensity of each assault were meticulously outlined.
- Benchmark models: The performance of the proposed technique was evaluated in comparison to several standard models. These standards were used to represent common security solutions and alternate methods for similar CPS applications.
- Simulation parameters: The settings for the simulation were determined by defining a number of parameters. This comprised the number of participants, the extent of their communications, the devices’ processing power, the structure of the network, and the volume of its traffic.
- Data generation: Synthetic data were developed to represent realistic CPS situations. This included data collected by sensors, instructions given to the system, and communications passed between its many parts. The data was carefully crafted with the CPS model under evaluation in mind.
- Evaluation metrics: The algorithm’s effectiveness and efficiency were measured with established criteria. It was ensured that the system was reliable in terms of detection accuracy, response time, false positive rate, false negative rate, and overall reliability. The metrics were chosen with the intention of giving a complete picture of the algorithm’s efficiency.
- Simulation execution: The simulation was executed using suitable simulation tools or programming frameworks. We used the predefined system model, attack scenarios, benchmark models, and simulation settings to run the simulations. Multiple simulation runs were performed to ensure reliability and statistical significance of the findings.
- Result analysis: Success criteria were established, and the resulting simulation data was evaluated and analysed. Statistical methods were used to decipher the results, and visualisation programmes were employed to examine differences between the suggested algorithm’s performance and that of the benchmark models. The goal of this evaluation was to identify strengths and weaknesses in the proposed algorithm.
6. Results
7. Security Attack Comparative Analysis
8. Discussion
9. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Shah, A.A.; Piro, G.; Grieco, L.A.; Boggia, G. A qualitative cross-comparison of emerging technologies for software-defined systems. In Proceedings of the 2019 Sixth International Conference on Software Defined Systems (SDS), Rome, Italy, 10–13 June 2019; pp. 138–145. [Google Scholar]
- Ali, A.; Mehboob, M. Comparative analysis of selected routing protocols for wlan based wireless sensor networks (wsns). In Proceedings of the 2nd International Multi-Disciplinary Conference, Lahore, Pakistan, 19–20 December 2016; Volume 19, p. 20. [Google Scholar]
- Shah, A.A.; Piro, G.; Grieco, L.A.; Boggia, G. A review of forwarding strategies in transport software-defined networks. In Proceedings of the 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 19–23 July 2020; pp. 1–4. [Google Scholar]
- Bruce, R.R.; Cunard, J.P.; Director, M.D. From Telecommunications to Electronic Services: A Global Spectrum of Definitions, Boundary Lines, and Structures; Butterworth-Heinemann: Oxford, UK, 2014. [Google Scholar]
- Gatteschi, V.; Lamberti, F.; Demartini, C.; Pranteda, C.; Santamaría, V. Blockchain and smart contracts for insurance: Is the technology mature enough? Future Internet 2018, 10, 20. [Google Scholar] [CrossRef] [Green Version]
- Jia, B.; Zhou, T.; Li, W.; Liu, Z.; Zhang, J. A blockchain-based location privacy protection incentive mechanism in crowd sensing networks. Sensors 2018, 18, 3894. [Google Scholar] [CrossRef] [Green Version]
- Biswas, K.; Muthukkumarasamy, V. Securing smart cities using blockchain technology. In Proceedings of the 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Sydney, NSW, Australia, 12–14 December 2016; pp. 1392–1393. [Google Scholar]
- Fernández-Caramés, T.M.; Froiz-Míguez, I.; Blanco-Novoa, O.; Fraga-Lamas, P. Enabling the internet of mobile crowdsourcing health things: A mobile fog computing, blockchain and iot based continuous glucose monitoring system for diabetes mellitus research and care. Sensors 2019, 19, 3319. [Google Scholar] [CrossRef] [Green Version]
- Ali, A.; Naveed, M.; Mehboob, M.; Irshad, H.; Anwar, P. An interference aware multi-channel mac protocol for wasn. In Proceedings of the 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT), Karachi, Pakistan, 5–7 April 2017; pp. 1–9. [Google Scholar]
- Beebeejaun, A. Vat on foreign digital services in mauritius; a comparative study with south africa. Int. J. Law Manag. 2020, 63, 239–250. [Google Scholar] [CrossRef]
- Shah, A.A.; Piro, G.; Grieco, L.A.; Boggia, G. A quantitative cross-comparison of container networking technologies for virtualized service infrastructures in local computing environments. Trans. Emerg. Telecommun. Technol. 2021, 32, e4234. [Google Scholar]
- Kim, H.; Kim, S.-H.; Hwang, J.Y.; Seo, C. Efficient privacy-preserving machine learning for blockchain network. IEEE Access 2019, 7, 136481–136495. [Google Scholar] [CrossRef]
- Cirstea, A.; Enescu, F.M.; Bizon, N.; Stirbu, C.; Ionescu, V.M. Blockchain technology applied in health the study of blockchain application in the health system (ii). In Proceedings of the 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania, 28–30 June 2018; pp. 1–4. [Google Scholar]
- Yazdinejad, A.; Srivastava, G.; Parizi, R.M.; Dehghantanha, A.; Choo, K.-K.R.; Aledhari, M. Decentralized authentication of distributed patients in hospital networks using blockchain. IEEE J. Biomed. Health Inform. 2020, 24, 2146–2156. [Google Scholar] [CrossRef]
- Patel, V. A framework for secure and decentralized sharing of medical imaging data via blockchain consensus. Health Inform. J. 2019, 25, 1398–1411. [Google Scholar] [CrossRef]
- El-Rewini, Z.; Sadatsharan, K.; Selvaraj, D.F.; Plathottam, S.J.; Ranganathan, P. Cybersecurity challenges in vehicular communications. Veh. Commun. 2020, 33, 100214. [Google Scholar] [CrossRef]
- Dorri, A.; Kanhere, S.S.; Jurdak, R.; Gauravaram, P. Blockchain for iot security and privacy: The case study of a smart home. In Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA, 13–17 March 2017; pp. 618–623. [Google Scholar]
- Hang, L.; Kim, D.-H. Design and implementation of an integrated iot blockchain platform for sensing data integrity. Sensors 2019, 19, 2228. [Google Scholar] [CrossRef] [Green Version]
- Yu, B.; Kermanshahi, S.K.; Sakzad, A.; Nepal, S. Chameleon hash time-lock contract for privacy preserving payment channel networks. In International Conference on Provable Security; Springer: Berlin/Heidelberg, Germany, 2019; pp. 303–318. [Google Scholar]
- Hameed, K.; Ali, A.; Naqvi, M.H.; Jabbar, M.; Junaid, M.; Haider, A. Resource management in operating systems-a survey of scheduling algorithms. In Proceedings of the International Conference on Innovative Computing (ICIC), Mataram, Indonesia, 28–29 October 2016; Volume 1. [Google Scholar]
- Jung, Y.; Peradilla, M.; Agulto, R. Packet key-based end-to-end security management on a blockchain control plane. Sensors 2019, 19, 2310. [Google Scholar] [CrossRef] [Green Version]
- Esposito, C.; Santis, A.D.; Tortora, G.; Chang, H.; Choo, K.-K.R. Blockchain: A panacea for healthcare cloud-based data security and privacy? IEEE Cloud Comput. 2018, 5, 31–37. [Google Scholar] [CrossRef]
- Choo, C.W. Information Management for the Intelligent Organization: The Art of Scanning the Environment; Information Today, Inc.: Medford, NJ, USA, 2002. [Google Scholar]
- Kermanshahi, S.K.; Liu, J.K.; Steinfeld, R.; Nepal, S.; Lai, S.; Loh, R.; Zuo, C. Multi-client cloud-based symmetric searchable encryption. IEEE Trans. Dependable Secur. Comput. 2019, 18, 2419–2437. [Google Scholar] [CrossRef]
- Kermanshahi, S.K.; Liu, J.K.; Steinfeld, R.; Nepal, S. Generic multi-keyword ranked search on encrypted cloud data. In European Symposium on Research in Computer Security; Springer: Berlin/Heidelberg, Germany, 2019; pp. 322–343. [Google Scholar]
- Dwivedi, A.D.; Srivastava, G.; Dhar, S.; Singh, R. A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 2019, 19, 326. [Google Scholar] [CrossRef] [Green Version]
- Rathi, V.K.; Chaudhary, V.; Rajput, N.K.; Ahuja, B.; Jaiswal, A.K.; Gupta, D.; Elhoseny, M.; Hammoudeh, M. A blockchain-enabled multi domain edge computing orchestrator. IEEE Internet Things Mag. 2020, 3, 30–36. [Google Scholar] [CrossRef]
- Nkenyereye, L.; Adhi Tama, B.; Shahzad, M.K.; Choi, Y.-H. Secure and blockchain-based emergency driven message protocol for 5G enabled vehicular edge computing. Sensors 2020, 20, 154. [Google Scholar] [CrossRef] [Green Version]
- Feng, C.; Yu, K.; Bashir, A.K.; Al-Otaibi, Y.D.; Lu, Y.; Chen, S.; Zhang, D. Efficient and secure data sharing for 5G flying drones: A blockchain-enabled approach. IEEE Netw. 2021, 35, 130–137. [Google Scholar] [CrossRef]
- Khujamatov, K.; Reypnazarov, E.; Akhmedov, N.; Khasanov, D. Blockchain for 5G Healthcare architecture. In Proceedings of the 2020 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 4–6 November 2020; pp. 1–5. [Google Scholar]
- Vivekanandan, M.; Sastry, V.N.; Reddy, U.S. BIDAPSCA5G: Blockchain based Internet of Things (IoT) device to device authentication protocol for smart city applications using 5G technology. Peer-Peer Netw. Appl. 2021, 14, 403–419. [Google Scholar] [CrossRef]
- Gao, J.; Agyekum, K.O.-B.O.; Sifah, E.B.; Acheampong, K.N.; Xia, Q.; Du, X.; Guizani, M.; Xia, H. A blockchain-SDN-enabled Internet of vehicles environment for fog computing and 5G networks. IEEE Internet Things J. 2019, 7, 4278–4291. [Google Scholar] [CrossRef]
- Zhou, S.; Huang, H.; Chen, W.; Zhou, P.; Zheng, Z.; Guo, S. pirate: A blockchain-based secure framework of distributed machine learning in 5g networks. IEEE Netw. 2020, 34, 84–91. [Google Scholar] [CrossRef]
- Zhang, Y.; Wang, K.; Moustafa, H.; Wang, S.; Zhang, K. Guest Editorial: Blockchain and AI for Beyond 5G Networks. IEEE Netw. 2020, 34, 22–23. [Google Scholar] [CrossRef]
- Yazdinejad, A.; Parizi, R.M.; Dehghantanha, A.; Choo, K.-K.R. Blockchain-enabled authentication handover with efficient privacy protection in SDN-based 5G networks. IEEE Trans. Netw. Sci. Eng. 2019, 8, 1120–1132. [Google Scholar] [CrossRef] [Green Version]
- Zhao, Y.; Zhao, J.; Zhai, W.; Sun, S.; Niyato, D.; Lam, K.-Y. A survey of 6G wireless communications: Emerging technologies. In Future of Information and Communication Conference; Springer: Berlin/Heidelberg, Germany, 2021; pp. 150–170. [Google Scholar]
- Bhattacharya, P.; Tanwar, S.; Shah, R.; Ladha, L. Mobile edge computing-enabled blockchain framework—A survey. In Proceedings of ICRIC 2019; Springer: Berlin/Heidelberg, Germany, 2020; pp. 797–809. [Google Scholar]
- Kaushik, S. Blockchain and 5G-Enabled Internet of Things: Background and Preliminaries. In Blockchain for 5G-Enabled IoT; Springer: Berlin/Heidelberg, Germany, 2021; pp. 3–31. [Google Scholar]
- Mistry, I.; Tanwar, S.; Tyagi, S.; Kumar, N. Blockchain for 5G-enabled IoT for industrial automation: A systematic review, solutions, and challenges. Mech. Syst. Signal Process. 2020, 135, 106382. [Google Scholar] [CrossRef]
- Budhiraja, I.; Tyagi, S.; Tanwar, S.; Kumar, N.; Guizani, M. CR-NOMA Based Interference Mitigation Scheme for 5G Femtocells Users. In Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 9–13 December 2018; Volume 1, pp. 1–6. [Google Scholar] [CrossRef]
- Kermanshahi, S.K.; Liu, J.K.; Steinfeld, R. Multi-user cloud-based secure keyword search. In Australasian Conference on Information Security and Privacy; Springer: Berlin/Heidelberg, Germany, 2017; pp. 227–247. [Google Scholar]
- Daraghmi, E.-Y.; Daraghmi, Y.-A.; Yuan, S.-M. MedChain: A Design of Blockchain-Based System for Medical Records Access and Permissions Management. IEEE Access 2019, 7, 164595–164613. [Google Scholar] [CrossRef]
- Dabbagh, M.; Kakavand, M.; Tahir, M.; Amphawan, A. Performance analysis of blockchain platforms: Empirical evaluation of hyperledger fabric and ethereum. In Proceedings of the IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia, 26–27 September 2020. [Google Scholar]
- Yang, G.; Lee, K.; Lee, K.; Yoo, Y.; Lee, H.; Yoo, C. Resource Analysis of Blockchain Consensus Algorithms in Hyperledger Fabric. IEEE Access 2022, 10, 74902–74920. [Google Scholar] [CrossRef]
- Wang, R.; Ye, K.; Meng, T.; Xu, C.-Z. Resource Analysis of Blockchain Consensus Algorithms in Hyperledger Fabric. In Proceedings of the 17th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, 18–20 September 2020. [Google Scholar]
- Tyagi, A.K.; Sreenath, N. Cyber Physical Systems: Analyses, challenges and possible solutions. Internet Things-Cyber-Phys. Syst. 2021, 1, 22–33. [Google Scholar] [CrossRef]
- Nair, M.M.; Tyagi, A.K.; Goyal, R. Medical cyber physical systems and its issues. Procedia Comput. Sci. 2019, 165, 647–655. [Google Scholar] [CrossRef]
- Liu, X.; Xu, B.; Wang, X.; Zheng, K.; Chi, K.; Tian, X. Impacts of sensing energy and data availability on throughput of energy harvesting cognitive radio networks. IEEE Trans. Veh. Technol. 2022, 72, 747–759. [Google Scholar] [CrossRef]
- Wang, R.; Ye, K.; Meng, T.; Xu, C.-Z. Performance evaluation on blockchain systems: A case study on Ethereum, Fabric, Sawtooth and Fisco-Bcos. In Proceedings of the Services Computing–SCC 2020: 17th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, 18–20 September 2020; pp. 120–134. [Google Scholar]
- Ali, A.; Al-rimy, B.A.S.; S. Alsubaei, F.; Almazroi, A.A.; Almazroi, A.A. HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications. Sensors 2023, 23, 6762. [Google Scholar] [CrossRef]
- Ali, A.; Almaiah, M.A.; Hajjej, F.; Pasha, M.F.; Fang, O.H.; Khan, R.; Teo, J.; Zakarya, M. An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network. Sensors 2022, 22, 572. [Google Scholar] [CrossRef]
- Almaiah, M.A.; Hajjej, F.; Ali, A.; Pasha, M.F.; Almomani, O. A Novel Hybrid Trustworthy Decentralized Authentication and Data Preservation Model for Digital Healthcare IoT Based CPS. Sensors 2022, 22, 1448. [Google Scholar] [CrossRef]
- Ali, A.; Pasha, M.F.; Guerrieri, A.; Guzzo, A.; Sun, X.; Saeed, A.; Hussain, A.; Fortino, G. A Novel Homomorphic Encryption and Consortium Blockchain-based Hybrid Deep Learning Model for Industrial Internet of Medical Things. IEEE Trans. Netw. Sci. Eng. 2023; in press. [Google Scholar] [CrossRef]
Abbreviation | Description |
---|---|
CPSs | Cyber-Physical Systems |
ACL | Access Control List |
DB | Database |
IoT | Internet of Things |
PoW | Proof of Work |
PoS | Proof of Stake |
EHR | Electronic Health Record |
GDPR | General Data Protection Regulation |
HIPAA | Health Insurance Portability and Accountability Act |
Model | Security Issues | Research Gaps | Problem | Solution |
---|---|---|---|---|
Traditional encryption-based model | Lack of data transparency | Limited scalability for large-scale systems | Difficulty in managing encryption keys | Explore the use of homomorphic encryption to allow data processing on encrypted data |
Vulnerability to key-based attacks | Inadequate protection against insider threats | Lack of adaptability to dynamic environments | Develop techniques for secure key management and continuous monitoring of user activities | |
Access control-based model | Limited granularity in access control policies | Complex management of access control rules | Insufficient support for context-aware access control | Investigate attribute-based access control (ABAC) with dynamic policy enforcement |
Difficulty in handling user revocation | Inability to address data sharing across multiple domains | Lack of fine-grained auditing capabilities | Explore the use of blockchain-based access control mechanisms and distributed ledgers | |
Centralized database-based model | Single point of failure | Potential data breaches due to centralization | Limited transparency and accountability | Investigate the use of distributed databases or decentralized storage systems |
Scalability limitations for large datasets | Dependency on trust in the centralized authority | Difficulty in ensuring data integrity | Explore distributed consensus algorithms for decentralized data management |
Benchmark Model | Number of Transactions | Execution Time (Seconds) |
---|---|---|
Proposed | 1000 | 15.6 |
[1] | 1000 | 22.3 |
[5] | 1000 | 18.9 |
[6] | 5000 | 78.2 |
[13] | 5000 | 105.9 |
[25] | 5000 | 92.7 |
[25] | 10,000 | 156.8 |
[8] | 10,000 | 215.4 |
[38] | 10,000 | 189.6 |
Attack Type | Benchmark Model [12] | Benchmark Model [13] |
---|---|---|
Denial of service (DoS) | High vulnerability to DoS attacks due to insufficient network resources allocation | Effective DoS attack prevention mechanisms in place |
Man-in-the-middle (MitM) | Vulnerable to MitM attacks due to weak encryption protocols | Robust encryption and authentication protocols to mitigate MitM attacks |
Phishing | Lack of effective phishing detection and prevention mechanisms | Advanced phishing detection techniques implemented |
Malware | Prone to malware infections and lacking effective malware detection | Robust malware detection and prevention mechanisms |
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Ali, A.; Al-rimy, B.A.S.; Almazroi, A.A.; Alsubaei, F.S.; Almazroi, A.A.; Saeed, F. Securing Secrets in Cyber-Physical Systems: A Cutting-Edge Privacy Approach with Consortium Blockchain. Sensors 2023, 23, 7162. https://doi.org/10.3390/s23167162
Ali A, Al-rimy BAS, Almazroi AA, Alsubaei FS, Almazroi AA, Saeed F. Securing Secrets in Cyber-Physical Systems: A Cutting-Edge Privacy Approach with Consortium Blockchain. Sensors. 2023; 23(16):7162. https://doi.org/10.3390/s23167162
Chicago/Turabian StyleAli, Aitizaz, Bander Ali Saleh Al-rimy, Abdulwahab Ali Almazroi, Faisal S. Alsubaei, Abdulaleem Ali Almazroi, and Faisal Saeed. 2023. "Securing Secrets in Cyber-Physical Systems: A Cutting-Edge Privacy Approach with Consortium Blockchain" Sensors 23, no. 16: 7162. https://doi.org/10.3390/s23167162