The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi
hotspots, and distributed networks like Tor has drastically complicated the task of identifying …

Code authorship attribution is the process of identifying the author of a given code. With
increasing numbers of malware and advanced mutation techniques, the authors of malware …

In a membership inference attack, an attacker aims to infer whether a data sample is in a
target classifier's training dataset or not. Specifically, given a black-box access to the target …

In today's cyber warfare realm, every stakeholder in cyberspace is becoming more potent by
developing advanced cyber weapons. They have equipped with the most advanced …

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 …

In this paper, we present a novel attack against authorship attribution of source code. We
exploit that recent attribution methods rest on machine learning and thus can be deceived by …

Efficient extraction of code authorship attributes is key for successful identification. However,
the extraction of such attributes is very challenging, due to various programming language …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Abstract Machine learning approaches to source code authorship attribution attempt to find
statistical regularities in human-generated source code that can identify the author or …

Hunting binary code difference without source code (ie, binary diffing) has compelling
applications in software security. Due to the high variability of binary code, existing solutions …