Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart Healthcare
In this paper, a novel Elliptic Crypt with Secured Blockchain-backed Federated Q-Learning Framework is proposed to offer an intelligent healthcare system that mitigates the attacks and data misused by malicious intruders. Initially, the entered IoMT data is collected from publicly available datasets...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10478731/ |
| _version_ | 1797227304680685568 |
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| author | Sudhakaran Gajendran Revathi Muthusamy Krithiga Ravi Omkumar Chandraumakantham Suguna Marappan |
| author_facet | Sudhakaran Gajendran Revathi Muthusamy Krithiga Ravi Omkumar Chandraumakantham Suguna Marappan |
| author_sort | Sudhakaran Gajendran |
| collection | DOAJ |
| description | In this paper, a novel Elliptic Crypt with Secured Blockchain-backed Federated Q-Learning Framework is proposed to offer an intelligent healthcare system that mitigates the attacks and data misused by malicious intruders. Initially, the entered IoMT data is collected from publicly available datasets and encrypted using the Extended Elliptic Curve Cryptography (E_ECurCrypt) technique for ensuring the security. This encrypted data is fed as an input to the blockchain-powered collaborative learning model. Here, the federated Q-learning model trains the inputs and analyzes the presented attacks to ensure better privacy protection. Afterwards, the data is securely stored in decentralized blockchain technology. Subsequently, an effective Delegated Proof of Stake (Del_PoS) consensus algorithm is used to validate the proposed framework. The experiment is conducted using the WUSTL-EHMS-2020 dataset and the performances are analyzed by evaluating multiple matrices and compared to other existing methods. The performance of the proposed framework can be assessed using multiple matrices and the results will be compared to other existing methods. As a result, the proposed method has achieved 99.23% accuracy, 98.42% precision, 98.12% recall, 98.27% F1 score, 59080.506 average throughput, 59080.506 average decryption time 1.94 seconds and an average encryption time of 1.84 seconds and are superior to conventional methods. |
| first_indexed | 2024-04-24T14:38:41Z |
| format | Article |
| id | doaj.art-95a26cd0f8024906b6f4ec80f500184a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T14:38:41Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-95a26cd0f8024906b6f4ec80f500184a2024-04-02T23:00:44ZengIEEEIEEE Access2169-35362024-01-0112459234593510.1109/ACCESS.2024.338152810478731Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart HealthcareSudhakaran Gajendran0https://orcid.org/0000-0002-0273-4185Revathi Muthusamy1https://orcid.org/0000-0003-3194-2968Krithiga Ravi2https://orcid.org/0000-0002-8842-1947Omkumar Chandraumakantham3https://orcid.org/0000-0003-2866-0281Suguna Marappan4https://orcid.org/0000-0002-0830-1110School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, IndiaIn this paper, a novel Elliptic Crypt with Secured Blockchain-backed Federated Q-Learning Framework is proposed to offer an intelligent healthcare system that mitigates the attacks and data misused by malicious intruders. Initially, the entered IoMT data is collected from publicly available datasets and encrypted using the Extended Elliptic Curve Cryptography (E_ECurCrypt) technique for ensuring the security. This encrypted data is fed as an input to the blockchain-powered collaborative learning model. Here, the federated Q-learning model trains the inputs and analyzes the presented attacks to ensure better privacy protection. Afterwards, the data is securely stored in decentralized blockchain technology. Subsequently, an effective Delegated Proof of Stake (Del_PoS) consensus algorithm is used to validate the proposed framework. The experiment is conducted using the WUSTL-EHMS-2020 dataset and the performances are analyzed by evaluating multiple matrices and compared to other existing methods. The performance of the proposed framework can be assessed using multiple matrices and the results will be compared to other existing methods. As a result, the proposed method has achieved 99.23% accuracy, 98.42% precision, 98.12% recall, 98.27% F1 score, 59080.506 average throughput, 59080.506 average decryption time 1.94 seconds and an average encryption time of 1.84 seconds and are superior to conventional methods.https://ieeexplore.ieee.org/document/10478731/Ciphertextsconsensus mechanismECC methodend-devicesencryption and decryptionMarkov decision process |
| spellingShingle | Sudhakaran Gajendran Revathi Muthusamy Krithiga Ravi Omkumar Chandraumakantham Suguna Marappan Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart Healthcare IEEE Access Ciphertexts consensus mechanism ECC method end-devices encryption and decryption Markov decision process |
| title | Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart Healthcare |
| title_full | Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart Healthcare |
| title_fullStr | Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart Healthcare |
| title_full_unstemmed | Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart Healthcare |
| title_short | Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart Healthcare |
| title_sort | elliptic crypt with secured blockchain assisted federated q learning framework for smart healthcare |
| topic | Ciphertexts consensus mechanism ECC method end-devices encryption and decryption Markov decision process |
| url | https://ieeexplore.ieee.org/document/10478731/ |
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