Routine Pattern Discovery and Anomaly Detection in Individual Travel Behavior

Temporal Pattern Discovery for Anomaly Detection in a Smart Home

The temporal nature of data collected in a smart environment provides us with a better understanding of patterns over time. Detecting anomalies in such datasets is a complex and challenging task. To solve this problem, we suggest a solution using temporal relations. Temporal pattern discovery based on modified Allen’s temporal relations [5] has helped discover interesting patterns and relations...

Behavior Clustering for Anomaly Detection

This paper aims to address the problem of clustering behaviors captured in surveillance videos for the applications of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior modeling and anomaly detection without any manual labeling of the training data set. The framework consists of the following key components: 1) Drawing from natural l...

Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction

Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The result...

Unsupervised topic discovery by anomaly detection

Detecting Novel Scans Through Pattern Anomaly Detection

We introduce a technique for detecting anomalous patterns in a categorical feature (one that takes values from a finite alphabet). It differs from most anomaly detection methods used to date in that it does not require attackfree training data, and it improves upon previous methods known to us in that it is aware when it is adequately trained to generate meaningful alerts, and it models data no...