Jason A. Laska

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This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labeled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly(More)
A trend in machine learning is the application of existing algorithms to ever-larger datasets. Support Vector Machines (SVM) have been shown to be very effective, but have been difficult to scale to large-data problems. Some approaches have sought to scale SVM training by approximating and parallelizing the underlying quadratic optimization problem. This(More)
Intrusion detection is often described as having two main approaches: signature-based and anomaly-based. We argue that only unsupervised methods are suitable for detecting anomalies. However, there has been a tendency in the literature to conflate the notion of an anomaly with the notion of a malicious event. As a result, the methods used to discover(More)
The consolidation of cyber communications networks and physical control systems within the energy smart grid introduces a number of new risks. Unfortunately, these risks are largely unknown and poorly understood, yet include very high impact losses from attack and component failures. One important aspect of risk management is the detection of anomalies and(More)
The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated(More)
This work presents a modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in streaming graph data. Our goal is to detect changes at multiple levels of granularity, thereby identifying specific nodes and subgraphs causing a graph to appear anomalously. In particular, the framework(More)
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