A Network Intrusion Detection System with Broadband WO_3–x/WO_3–x-Ag/WO_3–x Optoelectronic Memristor

Real-time intrusion detection system based on the von Neumann architecture struggle to balance low power consumption and high computing speed. In this work, a strategy for network intrusion detection system based on the WO_3–x/WO_3–x-Ag/WO_3–x structured optoelectronic memristor overcoming the aforementioned issues is proposed and demonstrated. Through the modulation of electrical signals, the memristor successfully simulates a series of important synaptic functionalities including short-term/long-term synaptic plasticity. Meanwhile, when subjected to light stimulus, it demonstrates remarkable synaptic behaviors in terms of long/short-term memory and “learning-forgetting-relearning.” Based on this memristor array, a convolutional neural network is constructed to recognize abnormal network records within the KDDCup-99 dataset accurately and efficiently. The power consumption (10^–6 W) is over seven orders of magnitude lower than that of central processing unit, etc. Subsequently, an intrusion detection system is established to integrate collection, processing, and detection of real-time network data, successfully classifying various types of network records. Hence, this work is expected to promote the development of high-density storage and neuromorphic computing technology, and provides an application idea for intelligent electronic devices.