5 papers
When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned
Sima Arasteh, Christophe Hauser
In recent years, machine learning has demonstrated impressive results in various fields, including software vulnerability detection. Nonetheless, using machine learning to identify…
Data Flows in You: Benchmarking and Improving Static Data-flow Analysis on Binary Executables
Nicolaas Weideman, Sima Arasteh, Mukund Raghothaman +2
Data-flow analysis is a critical component of security research. Theoretically, accurate data-flow analysis in binary executables is an undecidable problem, due to complexities of…
BinPool: A Dataset of Vulnerabilities for Binary Security Analysis
Sima Arasteh, Georgios Nikitopoulos, Wei-Cheng Wu +4
The development of machine learning techniques for discovering software vulnerabilities relies fundamentally on the availability of appropriate datasets. The ideal dataset consists…
Trim My View: An LLM-Based Code Query System for Module Retrieval in Robotic Firmware
Sima Arasteh, Pegah Jandaghi, Nicolaas Weideman +4
The software compilation process has a tendency to obscure the original design of the system and makes it difficult both to identify individual components and discern their purpose…
Is This the Same Code? A Comprehensive Study of Decompilation Techniques for WebAssembly Binaries
Wei-Cheng Wu, Yutian Yan, Hallgrimur David Egilsson +4
WebAssembly is a low-level bytecode language designed for client-side execution in web browsers. The need for decompilation techniques that recover high-level source code from WASM…