In This paper describes probabilistic methods for novelty detection when using pattern recognition methods for fault monitoring of dynamic systems. The problem of novelty detection is particularly acute when prior knowledge and training data only allow one to construct an incomplete classification model. Allowance must be made in model design so that the classifier will be robust to data genera...

When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For a class of such models, we show that ...

Our research addresses constructing a dynamic normal profile for anomaly detection systems without requiring timeconsuming retraining. We propose to continuously update normal profiles by keeping the most recently employed patterns whose amount is dynamically determined. Active window adjustment through a simplified concept drift algorithm helps to keep relevant instances without having to cont...

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 grid sensors quickly detect, in real-time, any unusual events, whether natural faults or malicious, occur on grid. Existing bad-data detectors industry lack sophistication robustly broad types anomalies, especially those due emerging cyb...

OF DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Computer Science The University of New Mexico Albuquerque, New Mexico

Traditionally, research on graph theory focused on studying graphs that are static. However, almost all real networks are dynamic in nature and large in size. Quite recently, research areas for studying the topology, evolution, applications of complex evolving networks and processes occurring in them and governing them attracted attention from researchers. In this work, we review the significan...

Understanding of human dynamics has drawn attention to various areas. Due to the wide spread of positioning technologies that use GPS or public Wi-Fi, location information can be obtained with high spatial-temporal resolution as well as at low cost. By collecting set of individual location information in real time, monitoring of human dynamics is recently considered possible and is expected to ...

Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering the collective anomalous data and both sensitivity and robustness of the anomaly detection model, a sequential symbolic anomaly detection method is proposed and applied to the gas turbine fuel system. A structural Finite State Machine is used to evaluate posterior probabilities of obser...

Development of sophisticated anomaly detection and diagnosis methods for spacecraft is one of the important problems in space system operation. In this study, we propose a diagnosis method for spacecraft using probabilistic reasoning and statistical learning with Dynamic Bayesian Networks (DBNs). In this method, the DBNs are initially from priorknowledge, then modified or partly re-constructed ...

anomaly detection (ad) has recently become an important application of target detection in hyperspectral images. the reed-xialoi (rx) is the most widely used ad algorithm that suffers from “small sample size” problem. the best solution for this problem is to use dimensionality reduction (dr) techniques as a pre-processing step for rx detector. using this method not only improves the detection p...