An Automated Semantic Analysis Framework for Controller Variables Based on Network Traffic

Programmable logic controllers (PLCs) play a crucial role in various industrial manufacturing processes. Recent attack events show that attackers have a strong interest in controller variables of PLCs, including the device status and internal program logic. Detecting anomalous messages targeting PLC controller variables, which relies on the analysis of controller variable semantics, has proven to be an effective method for identifying such attacks. However, the proprietary nature of industrial control protocols (ICPs) poses a challenge to extracting the required semantics. In this paper, we propose an automated framework namedSePannerto extract the semantics of controller variables from proprietary ICPs based on network traffic. Specifically, we first collect multiple groups of interaction traffic of PLCs and perform the starting-aligned comparisons on them to locate the semantic fields directly. Then, we identify and investigate a new problem in semantic extraction — interference resulting from misordered messages — and propose a set of filtering criteria to eliminate it effectively. We evaluate SePanner using the S7COMM protocol, and the results indicate that SePanner can successfully extract the semantics of controller variables with 100% accuracy. Additionally, we employ SePanner to analyze 7 proprietary ICPs, successfully extracting the semantics of 63 controller variables and their 134 states. Additionally, we demonstrate the extensive applications of SePanner in multiple ICS security scenarios and present its better performance compared with existing ICP semantic analyzing tools.