Quantum machine learning methods for intrusion detection in software-defined networks

Software-defined network architecture is the preferred way to build large computer networks that require high responsiveness to change and a high degree of automation. The main feature of this architecture is the centralized management of the entire network from a single controller. However, this approach opens new opportunities for attacks on the network, making the controller their main target. This paper explores the possibility of applying quantum machine learning models to detect such attacks.