AI has provided us with the ability to automate tasks, extract information from vast amounts of
data, and synthesize media that is nearly indistinguishable from the real thing. However …

Ensuring security for modern IoT systems requires the use of complex methods to analyze
their software. One of the most in-demand methods that has repeatedly been proven to be …

Machine learning (ML) systems are rapidly increasing in size, are acquiring new
capabilities, and are increasingly deployed in high-stakes settings. As with other powerful …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

Lack of transparency in deep neural networks (DNNs) make them susceptible to backdoor
attacks, where hidden associations or triggers override normal classification to produce …

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem
that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily …

The automatic detection of software vulnerabilities is an important research problem.
However, existing solutions to this problem rely on human experts to define features and …

Deep learning has demonstrated its strengths in numerous binary analysis tasks, including
function boundary detection, binary code search, function prototype inference, value set …

The problem of cross-platform binary code similarity detection aims at detecting whether two
binary functions coming from different platforms are similar or not. It has many security …

Fuzzing and symbolic execution are two complementary techniques for discovering software
vulnerabilities. Fuzzing is fast and scalable, but can be ineffective when it fails to randomly …