Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Anomaly detection is an important problem with multiple applications, and thus has been
studied for decades in various research domains. In the past decade there has been a …

Advanced Persistent Threats (APTs) are difficult to detect due to their" low-and-slow" attack
patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Intelligent Transport Systems (ITS) is a developing technology that will significantly alter the
driving experience. In such systems, smart vehicles and Road-Side Units (RSUs) …

Anomaly detection on attributed networks aims at finding nodes whose patterns deviate
significantly from the majority of reference nodes, which is pervasive in many applications …

Anomalies are occurrences in a dataset that are in some way unusual and do not fit the
general patterns. The concept of the anomaly is typically ill defined and perceived as vague …

Performing anomaly detection on attributed networks concerns with finding nodes whose
patterns or behaviors deviate significantly from the majority of reference nodes. Its success …

Social media is often viewed as a sensor into various societal events such as disease
outbreaks, protests, and elections. We describe the use of social media as a crowdsourced …

Detecting botnets in a network is crucial because bots impact numerous areas such as cyber
security, finance, health care, law enforcement, and more. Botnets are becoming more …