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 …

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 …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

This survey paper cites some methods of graph based anomaly detection in the field of
information security, finance, cybersecurity, online social networks, health care, law …

Given sensor readings over time from a power grid, how can we accurately detect when an
anomaly occurs? A key part of achieving this goal is to use the network of power grid …

Authenticated lateral movement via compromised accounts is a common adversarial
maneuver that is challenging to discover with signature-or rules-based intrusion detection …

Similarity measures are used extensively in machine learning and data science algorithms.
The newly proposed graph Relative Hausdorff (RH) distance is a lightweight yet nuanced …

We propose a novel Swarm Intelligence-based approach for anomaly detection of dynamic
systems. Specifically, we consider observation processes for dynamic systems from a …

An important task in network analysis is the detection of anomalous events in a network time
series. These events could merely be times of interest in the network timeline or they could …

(57) ABSTRACT A method for detecting abnormal changes in real-time in dynamic graphs.
The method includes extracting, by a graph sampler, an active sampled graph from an …