Due to the exposure of data caused by open source code, traditional methods cannot block the transmission of attacked data packets, resulting in the inability of data to autonomously defend. Therefore, a network link data tampering autonomous defense system based on Snort is designed. In the hardware part of the system, a packet sniffer is adopted to capture Snort messages, and the layered decoding is integrated into text information through an information decoding module. The integrated text information is sent to the system database through the network for processing high-volume alert data and storing records. In the system software section, a network depth defense model based on Snort is constructed to achieve real-time detection and automatic interception of tampering attacks. Based on the transmission characteristics of information packets in the network, the transmission distance between different nodes is calculated and the location of defense nodes is determined. The data transmission path is derived when the link layer data is subjected to tampering attacks, and an autonomous defense function is constructed for data tampering, therefore the autonomous defense of data is achieved. Using wavelet denoising data processing technology to obtain time-series data, using inverse wavelet transform reconstruction to obtain denoised data, the design of an autonomous defense system for data tampering is completed. According to the experimental results, the system has a high density of secure transmission of network link data, and the maximum success rate of key recovery can reach 98%, demonstrating strong robustness.