Network Anomaly Detection with Quantum Machine Learning

网络入侵和网络攻击可能危及互联系统的安全，并对其运行造成不可逆转的损害。对于火车车队、自动驾驶汽车等关键系统来说尤其如此。入侵检测系统（IDS）与访问控制和加密技术等其他安全机制一起部署，以进一步保护系统安全。网络入侵检测可以通过识别网络监控数据集中的异常并寻找潜在的问题来实现。然而，建立有效的网络异常检测系统在理论和实践中面临着许多挑战。该项目的目标是设计和开发一个使用量子机器学习（QML）算法进行网络异常检测的框架。量子机器学习有可能革命性地改变检测网络异常的能力，以防止/防御网络入侵和网络攻击。这一伙伴关系的主要成果是实现了QML异常检测框架。该框架有可能实现：1）执行时间，容量和学习效率的巨大进步，以及2）异常检测能力的显着改进。

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.