Quantum-driven enhanced machine learning algorithm for intrusion detection in Internet of things environment

Abstract

Industry 4.0 and other advancements are made possible by an Internet of Things (IoT), one of the emerging technologies. In addition, security becomes a difficult problem when IoT devices communicate over wireless channels. It has recently been feasible to detect intrusion in an IoT environment using machine learning techniques. The amount of computation needed for training grows dramatically with huge volume of data. As the amount of data increases, the running time of many machine learning algorithms increases drastically. At the intersection of traditional machine learning and quantum computing, quantum machine learning promises to revolutionize the processing and analysis of large data sets by utilizing the special benefits of quantum physics. This work introduces Hybrid Quantum Neural Network (HQNN) contrived for overcoming these challenges and facilitate efficient Quantum enabled machine learning for real time intrusion detection systems. Real network traces are used to validate our proposed intrusion detection technology and our approach shows remarkable improvement in accuracy against conventional approaches.