NLP-inspired deep learning for binary code analysis demonstrates notable performance.
Considering the diverse Instruction Set Architectures (ISAs) on the market, it is important to …

Static detection of security vulnerabilities in binary programs is an important research field in
software supply chain security. However, existing vulnerability detection methods based on …

Widespread code reuse allows vulnerabilities to proliferate among a vast variety of firmware.
There is an urgent need to detect these vulnerable codes effectively and efficiently. By …

Generative large language models (LLMs) pre-trained on code have shown impressive
effectiveness in code generation, program repair, and document analysis. However, existing …

Software obfuscation techniques can prevent binary diffing techniques from locating
vulnerable code by obfuscating the third-party code, to achieve the purpose of protecting …

Binary code similarity is the foundation of many security and software engineering
applications. Recent works leverage deep neural networks (DNN) to learn a numeric vector …

1-day vulnerabilities are common in practice and have posed severe threats to end users, as
adversaries could learn from released patches to find them and exploit them. Reproducing 1 …

Binary semantic comparison and authorship verification are critical in many security
applications. They respectively focus on the functional semantic features and developers' …

Algorithm classification consists in determining which algorithm a program implements,
given a finite set of candidates. Classifiers are used in applications such malware …

Binary code similarity detection is used to calculate the code similarity of a pair of binary
functions or files, through a certain calculation method and judgment method. It is a …