Network Asset Detection Based on Weakly Supervised Learning

Abstract

Network asset detection technology is the basis for sorting out and counting Internet assets, timely managing vulnerable network assets and anomaly detection. Due to various shortcomings in existing methods for obtaining device fingerprint information, the accuracy of evaluation results for network asset devices is low and the functionality is single. For this reason, proposes a network asset detection method based on weakly supervised learning, which aims to simulate the real network data in the network environment as much as possible to detect network assets, achieve more accurate results, so as to connect with the task of sorting out Internet assets and timely warning network asset vulnerabilities. The results indicate that there is a significant improvement in the speed and accuracy of asset detection when using only 30% of labeled data.