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Decentralized Blockchain With Convolutional Neural Network Model for Security Attack Mitigation


Affiliations
1 Department of Computer Science and Engineering, Madurai Institute of Engineering and Technology, India., India
2 Department of Information Technology, DMI College of Engineering, India., India
     

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In recent era, there is a demand and a need for more effective solutions based on new technologies for detection and mitigation because of the limitations and current state of the methods. In this research, we propose the design of a distributed ledger that utilises a convolutional neural network as a layer of defence against intrusions carried out by malicious actors. The result of simulation shows that the proposed method achieves a better traffic flow than the existing methods.

Keywords

Blockchain, Convolutional Neural Network, Security, Mitigation, Passive Attacks, Traffic Flow.
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  • A. Hassanzadeh, R. Stoleru and J. Chen, “Efficient Flooding in Wireless Sensor Networks Secured with Neighborhood Keys”, Proceedings of International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 119-126, 2011.
  • E. Fadel, V.C. Gungor, L. Nassef, N. Akkari, M.A. Malik, S. Almasri and I.F. Akyildiz, “A Survey on Wireless Sensor Networks for Smart Grid”, Computer Communications, Vol. 71, pp. 22-33, 2015.
  • M. Shobana and S. Ramya, “An Optimized Hybrid Deep Neural Network Architecture for Intrusion Detection in Real‐Time IoT Networks”, Transactions on Emerging Telecommunications Technologies, Vol. 33, No. 12, pp. 4609-4614, 2022.
  • R. Ch and S. Ramachandran, “Robust Cyber-Physical System Enabled Smart Healthcare unit using Blockchain Technology”, Electronics, Vol. 11, No. 19, pp. 3070-3074, 2022.
  • Y. Kumar and S. Gupta, “Effectiveness of Machine and Deep Learning for Blockchain Technology in Fraud Detection and Prevention”, Proceedings of International Conference on Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development, pp. 287-307, 2023.
  • A. Bhandari and F. Kamalov, “Machine Learning and Blockchain Integration for Security Applications”, Proceedings of International Conference on Big Data Analytics and Intelligent Systems for Cyber Threat Intelligence, pp. 129-173, 2023.
  • K.T. Selvi and R. Thamilselvan, “Privacy-Preserving Healthcare Informatics using Federated Learning and Blockchain”, Proceedings of International Conference on Healthcare 4.0, pp. 1-26, 2022.
  • R. Chaganti and V. Ravi, “A Survey on Blockchain Solutions in DDoS Attacks Mitigation: Techniques, Open Challenges and Future Directions”, Computer Communications, Vol. 78, pp. 1-13, 2022.
  • R. Akter and D.S. Kim, “Iomt-Net: Blockchain Integrated Unauthorized UAV Localization using Lightweight Convolution Neural Network for Internet of Military Things”, IEEE Internet of Things Journal, Vol. 87, No. 1, pp. 1-14, 2022.
  • Q. Abu Al-Haija, “Detection of Fake Replay Attack Signals on Remote Keyless Controlled Vehicles using Pre-Trained Deep Neural Network”, Electronics, Vol. 11, No. 20, pp. 3376-3383, 2022.

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  • Decentralized Blockchain With Convolutional Neural Network Model for Security Attack Mitigation

Abstract Views: 174  |  PDF Views: 0

Authors

C. Berin Jones
Department of Computer Science and Engineering, Madurai Institute of Engineering and Technology, India., India
D. Jeba Kingsley
Department of Information Technology, DMI College of Engineering, India., India

Abstract


In recent era, there is a demand and a need for more effective solutions based on new technologies for detection and mitigation because of the limitations and current state of the methods. In this research, we propose the design of a distributed ledger that utilises a convolutional neural network as a layer of defence against intrusions carried out by malicious actors. The result of simulation shows that the proposed method achieves a better traffic flow than the existing methods.

Keywords


Blockchain, Convolutional Neural Network, Security, Mitigation, Passive Attacks, Traffic Flow.

References