Semantic Learning Based Cross-Platform Binary Vulnerability Search For IoT Devices

Abstract

The rapid development of Internet of Things (IoT) has triggered more security requirements than ever, especially in detecting vulnerabilities in various IoT devices. The widely used clone-based vulnerability search methods are effective on source code; however, their performance is limited in IoT binary search. In this article, we present IoTSeeker, a function semantic learning based vulnerability search approach for cross-platform IoT binary. First, we construct the function semantic graph to capture both the data flow and control flow information and encode lightweight semantic features of each basic block within the semantic graph as numerical vectors. Then, the embedding vector of the whole binary function is generated by feeding the numerical vectors of basic blocks to our customized semantics aware neural network model. Finally, the cosine distance of two embedding vectors is calculated to determine whether a binary function contains a known vulnerability. The experiments show that IoTSeeker outperforms the state-of-the-art approaches for identifying cross-platform IoT binary vulnerabilities. For example, compared to Gemini, IoTSeeker finds 12.68% more vulnerabilities in the top-50 candidates, and improves the value of AUC for 8.23%.