Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

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Modern Applied Statistics With S

https://www.robots.ox.ac.uk/~davidc/pubs/NDreview2014.pdf

Review: A review of novelty detection

With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysisis a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.

Outlier Analysis

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

https://www.math.tecnico.ulisboa.pt/~apires/PDFs/CSAP_PR05.pdf

On optimal reject rules and ROC curves

Outlier identification is important in many applications of multivariate analysis. Either because there is some specific interest in finding anomalous observations or as a pre-processing task before the application of some multivariate method, in order to preserve the results from possible harmful effects of those observations. It is also of great interest in discriminant analysis if, when predicting group membership, one wants to have the possibility of labelling an observation as ”does not belong to any of the available groups”. The identification of outliers in multivariate data is usually based on Mahalanobis distance. The use of robust estimates of the mean and the covariance matrix is advised in order to avoid the masking effect (Rousseeuw and von Zomeren, 1990; Rocke and Woodruff, 1996; Becker and Gather, 1999). However, the performance of these rules is still highly dependent of multivariate normality of the bulk of the data. The aim of the method here described is to remove this dependency. The first version of this method appeared in Santos-Pereira and Pires (2002). In this talk we discuss some refinements and also the relation with a recently proposed similar method (Hardin and Rocke, 2004).

/pdf/using-clustering-and-robust-estimators-to-detect-outliers-in-2babxhh8yu.pdf

Using Clustering and Robust Estimators to Detect Outliers in Multivariate Data.

Outlier identification is important in many applications of multivariate analysis. Either because there is some specific interest in finding anomalous observations or as a pre-processing task before the application of some multivariate method, in order to preserve the results from possible harmful effects of those observations. It is also of great interest in supervised classification (or discriminant analysis) if, when predicting group membership, one wants to have the possibility of labelling an observation as “does not belong to any of the available groups”. The identification of outliers in multivariate data is usually based on Mahalanobis distance. The use of robust estimates of the mean and the covariance matrix is advised in order to avoid the masking effect (Rousseeuw and Leroy, 1985; Rousseeuw and von Zomeren, 1990; Rocke and Woodruff, 1996; Becker and Gather, 1999). However, the performance of these rules is still highly dependent of multivariate normality of the bulk of the data. The aim of the method here described is to remove this dependence.

/pdf/detection-of-outliers-in-multivariate-data-a-method-based-on-3sltrj3vxz.pdf

Detection of Outliers in Multivariate Data: A Method Based on Clustering and Robust Estimators

The current technological evolution allows us to easily and quickly obtain more information, computing capacity, communication and connectivity, in addition to allowing new forms of collaboration between disperse networks of diversified actors. This new reality not only offers enormous potential for innovation and enhanced performance for organizations but also, extends beyond the classic boundaries to affect individuals, other organizations and society in general. At the same time, this reality makes the ability of organizations to uphold their competitive advantage more challenging, since the control of the elements of their operating environment decreases drastically as they increasingly control the elements of the same environment. This is how the digital transformation of organizations becomes unavoidable, because otherwise they tend to disappear. In this context, it is necessary to infer our students’ methods for researching, identifying and taking solutions about if organizations in Portugal are already living the aforementioned digital transformation or if they are aware of the need to adapt to this new reality. The main goal of this research formatted as educational approach, is to evaluate and compare the current state of digital adoption in terms of the preparation according to the prevailing technological categories (pillars and innovation accelerators), as well as future priorities of organizations in the implementation of digital transformation in Portuguese organizations. To evaluate such objectives, a Project Based Learning (PBL) approach was used to reinforce the research and decision-making skills of undergraduate business students. Based on the results obtained, it can be concluded that organizations are aware of the need to accommodate the digital transformation not to fail and disappearing. However, it is not possible to conclude which strategy is to be adopted by the organizations, and how such a strategy will affect the organization as a whole, in particular as in respect of its business model.

http://repositorio.uportu.pt:8080/bitstream/11328/3013/1/An%20educational%20approach%20for%20present%20and%20future%20of%20Digital%20Transformation%20in%20Portuguese%20organizations.pdf

An Educational Approach for Present and Future of Digital Transformation in Portuguese Organizations

This study was developed under the scope of a Portuguese project focused on the entrepreneur’s perspective and perception on the internationalization process of his company: more specifically, about the factors that enhanced the company entry into foreign markets as well as the constraints found in this process. This work focuses on the importance of using digital transformation to integrate technological tools in international business practice and strategy and the obstacles encountered with introducing these new technologies. This study aims to determine the relationships between technology categories and obstacles. The final goal is to assess the impact of these characteristics of the companies by the sector of economic activity, size, and percentage of profits resulting from international expansion. A questionnaire was designed and sent by email to 8183 companies from the AICEP database, distributed by three main activity sectors. A total of 310 valid answers were gathered from the Portuguese internationalized companies. The research limitations are related to the reduced number of interviews. These interviews showed that managers were not aware of the concept of digital transformation and misunderstood the use of digital technologies in the internationalization process of the business. This limitation can add some bias to the qualitative results. In addition to these limitations, the number of responses per sector was also not homogeneous. The practical implications of this study are that managers and top-level executives can use that to better understand how companies could use digital tools and what obstacles they should avoid when they want to internationalize their business. This paper is one of the first research contributions to analyze the impact of digital transformation in the internalization of Portuguese companies.

/pdf/the-importance-of-digital-transformation-in-international-1yf8t5bg.pdf

Carla Santos-Pereira

Papers

On optimal reject rules and ROC curves

Using Clustering and Robust Estimators to Detect Outliers in Multivariate Data.

Detection of Outliers in Multivariate Data: A Method Based on Clustering and Robust Estimators

An Educational Approach for Present and Future of Digital Transformation in Portuguese Organizations

The Importance of Digital Transformation in International Business