QUANTUM-ENHANCED BLOCKCHAIN SECURITY: INTEGRATING QUANTUM COMPUTING WITH NETWORK ATTACK DETECTION

This paper describes a security framework that uses both blockchain technology and quantum-enhanced anomaly detection. We propose use of blockchain to create an unchangeable record of security events and smart contracts to automatically respond to threats that have been confirmed. A variational quantum circuit (VQC) is the basis for our system's hybrid quantum-classical model. The VQC processes information by turning classical data into quantum states, using parameterized gates to model complicated dependencies, and then measuring the result to classify it. We use a One-vs-Rest (OvR) method to find network attacks like Botnet, Brute Force, and Port Scan. We tested how well it worked in both perfect (noiseless) and simulated noisy quantum environments. The model was 93% accurate without noise and only 92% accurate with noise, which shows that it is strong. We found a major trade-off: the OvR method works well, but it costs a lot of computing power. This indicates that subsequent efforts should concentrate on creating more efficient quantum multiclass classification frameworks.