Concept drift poses a critical challenge to deploy machine learning models to solve practical
security problems. Due to the dynamic behavior changes of attackers (and/or the benign …

The success of deep learning (DL) techniques has led to their adoption in many fields,
including attack investigation, which aims to recover the whole attack story from logged …

Detecting semantically similar binary functions–a crucial capability with broad security
usages including vulnerability detection, malware analysis, and forensics–requires …

Accurate and robust disassembly of stripped binaries is challenging. The root of the difficulty
is that high-level structures, such as instruction and function boundaries, are absent in …

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 …

Given the huge success of automated software testing techniques, a large amount of
crashes is found in practice. Identifying the root cause of a crash is a time-intensive …

Fuzz drivers are needed for fuzzing libraries. A fuzz driver is a program which can execute
library functions by feeding them with inputs provided by the fuzzer. In practice, fuzz drivers …

Disassembly is the cornerstone of many binary analysis tasks. Traditional disassembly
approaches (eg, linear and recursive) are not accurate enough, while more sophisticated …

Although using machine learning techniques to solve computer security challenges is not a
new idea, the rapidly emerging Deep Learning technology has recently triggered a …

Online learning is widely used in production to refine model parameters after initial
deployment. This opens several vectors for covertly launching attacks against deployed …