Malware poses a severe threat to cyber security. Attackers use malware to achieve their
malicious purposes, such as unauthorized access, stealing confidential data, blackmailing …

With the rapid evolution of the Internet, the application of artificial intelligence fields is more
and more extensive, and the era of AI has come. At the same time, adversarial attacks in the …

Concept drift poses a critical challenge to deploy machine learning models to solve practical
security problems. Due to the dynamic behavior changes of attackers (and/or the benign …

We describe and release an open PE malware dataset called BODMAS to facilitate research
efforts in machine learning based malware analysis. By closely examining existing open PE …

Graph data, such as chemical networks and social networks, may be deemed
confidential/private because the data owner often spends lots of resources collecting the …

Portable document format (PDF) files are one of the most universally used file types. This
has incentivized hackers to develop methods to use these normally innocent PDF files to …

Machine learning (ML), especially deep learning (DL) techniques have been increasingly
used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has …

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

Randomized smoothing is a leading approach for constructing classifiers that are certifiably
robust against adversarial examples. Existing work on randomized smoothing has focused …

As an emerging security paradigm, machine learning (ML) based malicious traffic detection
is an essential part of automatic defense against network attacks. Powered by dedicated …