Quantum Machine Learning Driven Malicious User Prediction for Cloud Network Communications
Journal article
Authors | Gupta, R., Saxena. R., Gupta, I., Makkar, A. and Sing, A. K. |
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Abstract | This letter proposes a novel malicious user prediction model based on quantum machine learning that estimates the vicious entity present in the communication system precedently before allocating the data in the distributed environments. The proposed model scrutinizes the behavior of each user and estimates probable data breaches using a developed malicious user predictor unit. The model computes essential scores associated with each user request for the learning process of the prediction unit by generating training samples. The predictor unit exploits the computational and behavioral properties of Qubits and Quantum gates for the accurate prediction of the malicious user with high precision to grant access to non-malicious data requests only. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it significantly improves the security of the system up to 33.28% |
Keywords | Secure data allocation; malicious user; data breach |
Year | 2022 |
Journal | IEEE Networking Letters |
Journal citation | 4 (4), pp. 174-178 |
Publisher | IEEE |
ISSN | 2576-3156 |
Digital Object Identifier (DOI) | https://doi.org/https://doi.org/10.1109/LNET.2022.3200724 |
Web address (URL) | https://ieeexplore.ieee.org/document/9865138 |
Output status | Published |
Publication dates | |
Online | 23 Aug 2023 |
Publication process dates | |
Deposited | 01 Aug 2023 |
https://repository.derby.ac.uk/item/9zz35/quantum-machine-learning-driven-malicious-user-prediction-for-cloud-network-communications
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