Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions
Abstract With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includ...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-83564-4 |
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| author | R. Vijay Anand G. Magesh I. Alagiri Madala Guru Brahmam Balamurugan Balusamy Chithirai Pon Selvan Haya Mesfer Alshahrani Masresha Getahun Ben Othman Soufiene |
| author_facet | R. Vijay Anand G. Magesh I. Alagiri Madala Guru Brahmam Balamurugan Balusamy Chithirai Pon Selvan Haya Mesfer Alshahrani Masresha Getahun Ben Othman Soufiene |
| author_sort | R. Vijay Anand |
| collection | DOAJ |
| description | Abstract With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization. |
| format | Article |
| id | doaj-art-02494a17aee54a549ae0e989c23c732f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-02494a17aee54a549ae0e989c23c732f2025-01-12T12:24:37ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-83564-4Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactionsR. Vijay Anand0G. Magesh1I. Alagiri2Madala Guru Brahmam3Balamurugan Balusamy4Chithirai Pon Selvan5Haya Mesfer Alshahrani6Masresha Getahun7Ben Othman Soufiene8School of Computer Science Engineering and Information Systems, Vellore Institute of TechnologySchool of Computer Science Engineering and Information Systems, Vellore Institute of TechnologySchool of Computer Science Engineering and Information Systems, Vellore Institute of TechnologySchool of Computer Science Engineering and Information Systems, Vellore Institute of TechnologyShiv Nadar Institution of Eminence, Delhi-NCRCurtin University DubaiDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Science and Information Technology, College of Engineering and Technology, Kebri Dehar UniversityPRINCE Laboratory Research, ISITcom, Hammam Sousse, University of SousseAbstract With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization.https://doi.org/10.1038/s41598-024-83564-4BlockchainMachine learningFederated learningLSTM autoencodersPrivacy preserving |
| spellingShingle | R. Vijay Anand G. Magesh I. Alagiri Madala Guru Brahmam Balamurugan Balusamy Chithirai Pon Selvan Haya Mesfer Alshahrani Masresha Getahun Ben Othman Soufiene Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions Scientific Reports Blockchain Machine learning Federated learning LSTM autoencoders Privacy preserving |
| title | Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions |
| title_full | Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions |
| title_fullStr | Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions |
| title_full_unstemmed | Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions |
| title_short | Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions |
| title_sort | design of an improved model using federated learning and lstm autoencoders for secure and transparent blockchain network transactions |
| topic | Blockchain Machine learning Federated learning LSTM autoencoders Privacy preserving |
| url | https://doi.org/10.1038/s41598-024-83564-4 |
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