The emergence of the Internet of Things (IoT) technology has brought about tremendous
possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Machine-learning (ML) methods have gained prominence in the quantitative sciences.
However, there are many known methodological pitfalls, including data leakage, in ML …

Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …

The use of learning-based techniques to achieve automated software vulnerability detection
has been of longstanding interest within the software security domain. These data-driven …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

System auditing provides a low-level view into cyber threats by monitoring system entity
interactions. In response to advanced cyber-attacks, one prevalent solution is to apply data …

Fuzzing has proven to be a highly effective approach to uncover software bugs over the past
decade. After AFL popularized the groundbreaking concept of lightweight coverage …

Provenance graphs are structured audit logs that describe the history of a system's
execution. Recent studies have explored a variety of techniques to analyze provenance …

Despite decades of research in network traffic analysis and incredible advances in artificial
intelligence, network intrusion detection systems based on machine learning (ML) have yet …