5 papers
Trident: Improving Malware Detection with LLMs and Behavioral Features
Rebecca Saul, Jingzhi Jiang, Elliott Chia +1
Traditionally, machine learning methods for PE malware detection have relied on static features like byte histograms, string information, and PE header contents. One barrier to inc…
SCGAgent: Recreating the Benefits of Reasoning Models for Secure Code Generation with Agentic Workflows
Rebecca Saul, Hao Wang, Koushik Sen +1
Large language models (LLMs) have seen widespread success in code generation tasks for different scenarios, both everyday and professional. However current LLMs, despite producing…
Is Function Similarity Over-Engineered? Building a Benchmark
Rebecca Saul, Chang Liu, Noah Fleischmann +4
Binary analysis is a core component of many critical security tasks, including reverse engineering, malware analysis, and vulnerability detection. Manual analysis is often time-con…
Assemblage: Automatic Binary Dataset Construction for Machine Learning
Chang Liu, Rebecca Saul, Yihao Sun +5
Binary code is pervasive, and binary analysis is a key task in reverse engineering, malware classification, and vulnerability discovery. Unfortunately, while there exist large corp…
Lempel-Ziv Networks
Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz +3
Sequence processing has long been a central area of machine learning research. Recurrent neural nets have been successful in processing sequences for a number of tasks; however, th…