Quantum Machine Learning for Advanced Threat Detection in Cybersecurity

This study investigates the synergy between Classical Machine Learning (CML) and Quantum Machine Learning (QML) in analyzing security datasets, conducting a comparative analysis using models based on QML and CML to evaluate their performance as data sizes and iteration counts increase.The author, specifically, employs popular machine learning methods, including Support Vector Machines (SVM), Neural Networks (NN), and Logistic Regression (LR), to assess these techniques on real-world security datasets, such as network intrusion detection data and malware classification logs.The primary focus is determining the effectiveness and efficiency of QML and CML approaches in handling large-scale security data.Through rigorous experimentation, the study highlights the benefits and drawbacks of both QML and CML, indicating that while QML offers significant speedups in processing times for large datasets due to quantum parallelism, it faces challenges in terms of hardware accessibility and noise sensitivity, while CML methods, though slower with massive data, benefit from mature algorithms and more robust infrastructure.The outcomes provide critical insights into the practicality of applying QML and CML to security-related applications, demonstrating that QML techniques can outperform CML in specific scenarios, such as real-time threat detection, due to their superior computational efficiency.However, the current limitations of quantum hardware suggest that CML remains more practical for many applications in the short term.This work significantly advances the state of the art in Quantum Machine Learning.It offers vital guidance for practitioners and researchers in security data analysis, underscoring the potential of QML to revolutionize security data processing while acknowledging the ongoing need for advancements in quantum computing technology.