Anomaly Detection by Learning Dynamics From a Graph

A Protocol Graph Based Anomaly Detection System

Anomaly detection systems offer the potential to identify new attacks before signatures are identified. To do so, these systems build models of normal user activity from historical data and then use these models to identify deviations from normal behavior caused by attacks. In this thesis, we develop a method of anomaly detection using protocol graphs, graph-based representations of network tra...

Graph-based Image Anomaly Detection

RX Detector is recognized as the benchmark algorithm for image anomaly detection, however it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a highly dimensional covariance matrix and the inability to effectively include spatial awareness in its evaluation. In this work a novel graph-based solution to the ...

Learning Algorithms for Anomaly Detection from Images

Visual surveillance networks are installed in many sensitive places in the present world. Human security officers are required to continuously stare at large numbers of monitors simultaneously, and for lengths of time at a stretch. Constant alert vigilance for hours on end is difficult to maintain for human beings. It is thus important to remove the onus of detecting unwanted activity from the ...

ADAPTIVE GRAPH-BASED ALGORITHMS FOR CONDITIONAL ANOMALY DETECTION AND SEMI-SUPERVISED LEARNING by

Anomaly Detection on Graph Time Series

In this paper, we use variational recurrent neural network to investigate the anomaly detection problem on graph time series. The temporal correlation is modeled by the combination of recurrent neural network (RNN) and variational inference (VI), while the spatial information is captured by the graph convolutional network. In order to incorporate external factors, we use feature extractor to au...