Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

Co** with malware is getting more and more challenging, given their relentless growth in
complexity and volume. One of the most common approaches in literature is using machine …

Abstract Internet of Things (IoT) systems have become an intrinsic technology in various
industries and government services. Unfortunately, IoT devices and networks are known to …

Although deep learning has been applied to successfully address many data mining
problems, relatively limited work has been done on deep learning for anomaly detection …

In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed
serious and evolving security threats to Internet users. To protect legitimate users from these …

Recent results suggest that attacks against supervised machine learning systems are quite
effective, while defenses are easily bypassed by new attacks. However, the specifications for …

Learning expressive low-dimensional representations of ultrahigh-dimensional data, eg,
data with thousands/millions of features, has been a major way to enable learning methods …

The rapidly growing body of research in adversarial machine learning has demonstrated
that deep neural networks (DNNs) are highly vulnerable to adversarially generated images …

The steady growth in the number of deployed Internet of Things (IoT) devices has been
paralleled with an equal growth in the number of malicious software (malware) targeting …

Anomaly detection (AD) is a crucial task in machine learning with various applications, such
as detecting emerging diseases, identifying financial frauds, and detecting fake news …