Combining support vector machines and segmentation algorithms for efficient anomaly detection: a petroleum industry application

Proceedings of: International Joint Conference SOCO’14-CISIS’14-ICEUTE’14, Bilbao, Spain, June 25th–27th, 2014, ProceedingsAnomaly detection is the problem of finding patterns in data that do not conform to expected behavior. Similarly, when patterns are numerically distant from the rest of sample, anomalies are indicated as outliers. Anomaly detection had recently attracted the attention of the research community for real-world applications. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct, or react to the situations associated with them. In that sense, heavy extraction machines for pumping and generation operations like turbomachines are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. For dealing with this and with the lack of labeled data, in this paper we propose a combination of a fast and high quality segmentation algorithm with a one-class support vector machine approach for efficient anomaly detection in turbomachines. As result we perform empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.This work was partially funded by CNPq BJT Project 407851/2012-7 and CNPq PVE Project 314017/2013-

Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels

Many graph datasets are labelled with discrete and numeric attributes. Most frequent substructure discovery algorithms ignore numeric attributes; in this paper we show how they can be used to improve search performance and discrimination. Our thesis is that the most descriptive substructures are those which are normative both in terms of their structure and in terms of their numeric values. We explore the relationship between graph structure and the distribution of attribute values and propose an outlier-detection step, which is used as a constraint during substructure discovery. By pruning anomalous vertices and edges, more weight is given to the most descriptive substructures. Our method is applicable to multi-dimensional numeric attributes; we outline how it can be extended for high-dimensional data. We support our findings with experiments on transaction graphs and single large graphs from the domains of physical building security and digital forensics, measuring the effect on runtime, memory requirements and coverage of discovered patterns, relative to the unconstrained approach

A Multi-Resolution Approach for Atypical Behaviour Mining

International audienceAtypical behaviours are the basis of a valuable knowledge in domains related to security (e.g. fraud detection for credit card [1], cyber security [4] or safety of critical systems [6]). Atypicity generally depends on the isolation level of a (set of) records, compared to the dataset. One possible method for finding atypic records aims to perform two steps. The first step is a clustering (grouping the records by similarity) and the second step is the identification of clusters that do not correspond to a satisfying number of records. The main problem is to adjust the method and find the good level of atypicity. This issue is even more important in the domain of data streams, where a decision has to be taken in a very short time and the end-user does not want to try several settings. In this paper, we propose Mrab, a self-adjusting approach intending to automatically discover atypical behaviours (in the results of a clustering algorithm) without any parameter. We provide the formal framework of our method and our proposal is tested through a set of experiments

The Hypocritical Hegemon: How the United States Shapes Global Rules against Tax Evasion and Avoidance

In The Hypocritical Hegemon, Lukas Hakelberg takes a close look at how US domestic politics affects and determines the course of global tax policy. Through an examination of recent international efforts to crack down on offshore tax havens and the role the United States has played, Hakelberg uncovers how a seemingly innocuous technical addition to US law has had enormous impact around the world, particularly for individuals and corporations aiming to avoid and evade taxation. Through bullying and using its overwhelming political power, writes Hakelberg, the United States has imposed rules on the rest of the world while exempting domestic banks for the same reporting requirements. It can do so because no other government wields control over such huge financial and consumer markets. This power imbalance is at the heart of The Hypocritical Hegemon. Thanks to generous funding from COFFERS EU, the ebook editions of this book are available as Open Access volumes from Cornell Open (cornellopen.org) and other repositories