Security metrics have received significant attention. However, they have not been
systematically explored based on the understanding of attack-defense interactions, which …

Phishing and spear phishing are typical examples of masquerade attacks since trust is built
up through impersonation for the attack to succeed. Given the prevalence of these attacks …

Malware remains a big threat to cyber security, calling for machine learning based malware
detection. While promising, such detectors are known to be vulnerable to evasion attacks …

Malicious software (malware) is a major cyber threat that has to be tackled with Machine
Learning (ML) techniques because millions of new malware examples are injected into …

Machine learning-based malware detection is known to be vulnerable to adversarial
evasion attacks. The state-of-the-art is that there are no effective defenses against these …

Detecting software vulnerabilities is an important problem and a recent development in
tackling the problem is the use of deep learning models to detect software vulnerabilities …

Learning-based classifiers are found to be vulnerable to attacks by adversarial samples.
Some works suggested that ensemble classifiers tend to be more robust than single …

Machine Learning (ML) techniques can facilitate the automation of mal icious soft ware
(malware for short) detection, but suffer from evasion attacks. Many studies counter such …

In this paper we study the problem of content-based image retrieval. In this problem, the
most popular performance measure is the top precision measure, and the most important …

Abstract Machine learning (ML) based classifiers are vulnerable to evasion attacks, as
shown by recent attacks. However, there is a lack of systematic study of evasion attacks on …