An Anomaly Detection Model for Web Applications Based on Bionic Perception Mechanism

As network security threats become more diverse and complex, the traditional shell-like defense system that builds the web first and then provides defense is limited in coping with increasingly complex attack patterns. To address this issue, this paper is inspired by neuronal perception in biology and draws on the characteristics of biological neuronal perception: layered collaborative perception of information, signal transmission through discharge, low-level perception of primary stimuli, highlevel processing of complex tasks, and efficient division of labor and regulation of signals among cells. Based on these principles, a biological information perception model embedded within a specific web application is proposed. The model simulates the perception and recognition mechanism of biological neurons and transmits the information perceived inside the web application to the Spiking Neural Network (SNN) in the form of pulses to achieve accurate recognition of anomalies. To validate the effectiveness of the model, this paper applies it to a web application based on the Java layered architecture, and designs a neuron-type sensor module to monitor user requests and program events in real time. The experimental results show that the model can accurately locate the entry point of the attacked function, and the anomaly detection accuracy is on par with that of the traditional machine learning model, while exhibiting superior noise resistance.