Quantum Machine Learning Driven Malicious User Prediction for Cloud
Network Communications
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%.