Traffic analysis is a compound of strategies intended to find relationships, patterns,
anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic …

Despite considerable progress in genome-and proteome-based high-throughput screening
methods and in rational drug design, the increase in approved drugs in the past decade did …

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 …

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 …

Comparison of graph structure is a ubiquitous task in data analysis and machine learning,
with diverse applications in fields such as neuroscience, cyber security, social network …

Communication networks are the key enabling technology for our digital society. In order to
sustain their critical services in the future, communication networks need to flexibly …

Traditionally most of the anomaly detection algorithms have been designed
for'static'datasets, in which all the observations are available at one time. In non-stationary …

Can neural networks learn to compare graphs without feature engineering? In this paper, we
show that it is possible to learn representations for graph similarity with neither domain …

Ego-network, which represents relationships between a specific individual, ie, the ego, and
people connected to it, ie, alters, is a critical target to study in social network analysis …