Entrar
insight - Binary Code Similarity Detection
CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection
CEBin proposes a cost-effective framework that combines embedding-based and comparison-based approaches to enhance accuracy while minimizing overheads in large-scale binary code similarity detection.
CEBin: Cost-Effective Framework for Large-Scale Binary Code Similarity Detection
CEBin proposes a cost-effective framework that combines embedding-based and comparison-based approaches to enhance accuracy in large-scale binary code similarity detection.
A Comparative Evaluation of AI-powered Binary Code Similarity Detection Approaches
Despite the promising advancements of AI-powered binary code similarity detection (BinSD) techniques, particularly those based on graph neural networks (GNNs), there remains significant room for improvement, especially in addressing the "embedding collision" problem and enhancing their performance in real-world applications like vulnerability search.