Quantum-Driven Machine Learning Framework for Enhanced Detection of Malicious URLs in Cybersecurity

As cyber threats grow in sophistication, so must our detection methods. Traditional machine learning models are generally effective against simpler cyber behaviors but suffer from inefficiencies in dealing with high-dimensional data and being compromised by adversarial attacks. This paper explores the application of Quantum-Enhanced Machine Learning (QEML) models in the area of cybersecurity, specifically for malicious URL detection. We propose a hybrid quantum-classical technology leveraging the quantum computer’s unique capability to efficiently search a high-dimensional feature space. The proposed quantum-enhanced model was benchmarked against a classical project; on significant portions of the dataset, the quantum model proved to be vastly more accurate and robust when assessed against the classical techniques.