Amorphous Tungsten Oxide Homojunction‐Based Optoelectronic Memristor Array Mimicking Local Image Enhancement for Network Intrusion Detection System

Abstract The performance of network intrusion detection systems is constrained by factors related to data quality and the richness of feature information. Generic data preprocessing methods often struggle to effectively capture local features, which further diminishes their capacity to identify nonlinear and complex intrusion behaviors. Here, an optoelectronic memristor array with multiwavelength control based on oxygen‐rich and oxygen‐deficient amorphous tungsten oxide (WO x (OR)/WO x (OD)) is developed. The device demonstrated synaptic behavior under electronic stimulation. Interestingly, the device shows fast photoresponse and persistent photoconductivity behavior at visible and UV irradiation, respectively, through the modulation of oxygen vacancy ionization and de‐ionization processes in WO x (OD). Leveraging this phenomenon, a localized image enhancement strategy is effectively demonstrated on the device, showcasing substantial improvements in image contrast observed before and after the enhancement process. Furthermore, the localized enhancement approach, applied to the KDDCUP‐99 dataset, enables the convolutional neural network to better capture complex intrusion features, improving detection accuracy from 89.66% to 94.03%. This integrated approach introduces a novel technical pathway for the field of intrusion detection, seamlessly bridging image processing, neuromorphic computing, and cybersecurity applications.