While deep learning has shown a great potential in various domains, the lack of
transparency has limited its application in security or safety-critical areas. Existing research …

Recently popularized graph neural networks achieve the state-of-the-art accuracy on a
number of standard benchmark datasets for graph-based semi-supervised learning …

With the recent success of embeddings in natural language processing, research has been
conducted into applying similar methods to code analysis. Most works attempt to process the …

Binary code analysis allows analyzing binary code without having access to the
corresponding source code. A binary, after disassembly, is expressed in an assembly …

Binary code similarity detection (BCSD) has many applications, including patch analysis,
plagiarism detection, malware detection, and vulnerability search etc. Existing solutions …

Code reuse improves software development efficiency, however, vulnerabilities can be
introduced inadvertently. Many existing works compute the code similarity based on CFGs to …

We present a novel approach for predicting debug information in stripped binaries. Using
machine learning, we first train probabilistic models on thousands of non-stripped binaries …

Patching is the main resort to battle software vulnerabilities. It is critical to ensure that
patches are propagated to all affected software timely, which, unfortunately, is often not the …

Given a closed-source program, such as most of proprietary software and viruses, binary
code analysis is indispensable for many tasks, such as code plagiarism detection and …

Machine learning has become mainstream across industries. Numerous examples proved
the validity of it for security applications. In this work, we investigate how to reverse engineer …