1 citations · 1 across the 5 of their papers we have counts for
6 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…
The Reversing Machine: Reconstructing Memory Assumptions
Mohammad Sina Karvandi, Soroush Meghdadizanjani, Sima Arasteh +5
Existing anti-malware software and reverse engineering toolkits struggle with stealthy sub-OS rootkits due to limitations of run-time kernel-level monitoring. A malicious kernel-le…
Bin2vec: Learning Representations of Binary Executable Programs for Security Tasks
Shushan Arakelyan, Sima Arasteh, Christophe Hauser +2
Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time-consuming task for human analysts. In order to improv…