Enhancing the Feature Profiles of Web Shells by Analyzing the Performance of Multiple Detectors
Abstract
Web shells are commonly used to transfer malicious scripts in order to control web servers remotely. Malicious web shells are detected by extracting the feature profiles of known web shells and creating a learning model that classifies malicious samples. This chapter proposes a novel feature profile scheme for characterizing malicious web shells based on the opcode sequences and static properties of PHP scripts. A real-world dataset is employed to compare the performance of the feature profile scheme against state-of-art schemes using various machine learning algorithms. The experimental results demonstrate that the new feature profile scheme significantly reduces the false positive rate.
Domains
Computer Science [cs]| Origin | Files produced by the author(s) |
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