Network Anomaly Detection with Quantum Machine Learning

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Cyber-intrusions and cyber-attacks can endanger the security of connected systems and bring irreversible damages to their operation. This is particularly true for critical systems such as train fleets, autonomous vehicles, etc. Intrusion detection systems (IDS) are deployed in conjunction with other security mechanisms such as access control and encryption techniques to further secure systems. Network intrusion detection can be performed by identifying the abnormalities in network monitoring data sets and looking for potential problems. However, building effective systems for network anomaly detection faces many challenges in theory and in practice. The goal of the project is to design and develop a framework for network anomaly detection by using quantum machine learning (QML) algorithms. Quantum machine learning has the potential to revolutionarily transform the capacity to detect network anomalies so as to prevent/defend against cyber intrusions and cyber attacks. The main outcome of this partnership is the materialization of a framework for anomaly detection with QML. This framework has the potential to enable: 1) huge advances in execution time, capacity and learning efficiency and 2) significant improvements in anomaly detection capabilities.