In recent years, the Internet of Things (IoT) paradigm has been widely applied across a
variety of industrial and consumer areas to facilitate greater automation and increase …

Large language models (LLMs) have demonstrated significant poten-tial for many
downstream tasks, including those requiring human-level intelligence, such as vulnerability …

Currently, Android malware detection methods always focus on one kind of app feature,
such as structural, semantic, or other statistical features. This paper proposes a novel …

The complexity of modern software systems, the integration of several software components,
and the increasing exposure to public networks make systems more susceptible to cyber …

Contrary to prevailing wisdom, we argue that the measure of binary decompiler success is
not to eliminate all gotos or reduce the complexity of the decompiled code but to get as close …

Graph similarity learning is a significant and fundamental issue in the theory and analysis of
graphs, which has been applied in a variety of fields, including object tracking …

In the field of cybersecurity, the ability to compute similarity scores at the function level for
binary code is of utmost importance. Considering that a single binary file may contain an …

Continuous fuzzing has become an integral part of the Linux kernel ecosystem, discovering
thousands of bugs over the past few years. Interestingly, only a tiny fraction of them were …

In recent years there has been a shift from heuristics based malware detection towards
machine learning, which proves to be more robust in the current heavily adversarial threat …

Identifying compiler toolchain provenance serves as a basis for both benign and malicious
binary analyses. A wealth of prior studies mostly focuses on the inference of a popular …