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217
Anomaly Detection: A Survey
, 2007
"... 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 c ..."
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Cited by 449 (5 self)
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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 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 di®erent 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.
A survey of outlier detection methodologies
 Artificial Intelligence Review
, 2004
"... Abstract. Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populat ..."
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Cited by 261 (3 self)
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Abstract. Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review.
Mining DistanceBased Outliers in Near Linear Time with Randomization and a Simple Pruning Rule
, 2003
"... Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic ..."
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Cited by 146 (4 self)
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Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real highdimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the e#ciency is because the time to process nonoutliers, which are the majority of examples, does not depend on the size of the data set.
LOCI: Fast outlier detection using the local correlation integral
, 2003
"... Outlier detection is an integral part of data mining and has attracted much attention recently [8, 15, 19]. In this paper, we propose a new method for evaluating outlierness, which we call the Local Correlation Integral (LOCI). As with the best previous methods, LOCI is highly effective for detectin ..."
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Cited by 124 (12 self)
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Outlier detection is an integral part of data mining and has attracted much attention recently [8, 15, 19]. In this paper, we propose a new method for evaluating outlierness, which we call the Local Correlation Integral (LOCI). As with the best previous methods, LOCI is highly effective for detecting outliers and groups of outliers (a.k.a. microclusters). In addition, it offers the following advantages and novelties: (a) It provides an automatic, datadictated cutoff to determine whether a point is an outlier—in contrast, previous methods force users to pick cutoffs, without any hints as to what cutoff value is best for a given dataset. (b) It can provide a LOCI plot for each point; this plot summarizes a wealth of information about the data in the vicinity of the point, determining clusters, microclusters, their diameters and their intercluster distances. None of the existing outlierdetection methods can match this feature, because they output only a single number for each point: its outlierness score. (c) Our LOCI method can be computed as quickly as the best previous methods. (d) Moreover, LOCI leads to a practically linear approximate method, aLOCI (for approximate LOCI), which provides fast highlyaccurate outlier detection. To the best of our knowledge, this is the first work to use approximate computations to speed up outlier detection.
OddBall: Spotting Anomalies in Weighted Graphs
"... Abstract. Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ran ..."
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Cited by 70 (23 self)
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Abstract. Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ranks and eigenvalues that seem to govern the socalled “neighborhood subgraphs ” and we show how to use these rules for anomaly detection; (b) we carefully choose features, and design OddBall, so that it is scalable and it can work unsupervised (no userdefined constants) and (c) we report experiments on many real graphs with up to 1.6 million nodes, where OddBall indeed spots unusual nodes that agree with intuition. 1
Neighborhood formation and anomaly detection in bipartite graphs
 In ICDM
, 2005
"... Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nod ..."
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Cited by 65 (16 self)
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Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (Neighborhood formation), and 2) finding abnormal nodes (Anomaly detection). And we propose algorithms to compute the neighborhood for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify abnormal nodes, using neighborhood information. We evaluate the quality of neighborhoods based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets. 1
A novel anomaly detection scheme based on principal component classifier
 in Proceedings of the IEEE Foundations and New Directions of Data Mining Workshop, in conjunction with the Third IEEE International Conference on Data Mining (ICDM’03
, 2003
"... This paper proposes a novel scheme that uses robust principal component classifier in intrusion detection problem where the training data may be unsupervised. Assuming that anomalies can be treated as outliers, an intrusion predictive model is constructed from the major and minor principal component ..."
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Cited by 56 (5 self)
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This paper proposes a novel scheme that uses robust principal component classifier in intrusion detection problem where the training data may be unsupervised. Assuming that anomalies can be treated as outliers, an intrusion predictive model is constructed from the major and minor principal components of normal instances. A measure of the difference of an anomaly from the normal instance is the distance in the principal component space. The distance based on the major components that account for 50 % of the total variation and the minor components with eigenvalues less than 0.20 is shown to work well. The experiments with KDD Cup 1999 data demonstrate that our proposed method achieves 98.94 % in recall and 97.89 % in precision with the false alarm rate 0.92 % and outperforms the nearest neighbor method, densitybased local outliers (LOF) approach, and the outlier detection algorithms based on Canberra metric.
Using Probabilistic Models for Data Management in Acquisitional Environments
, 2005
"... Traditional database systems, particularly those focused on capturing and managing data from the real world, are poorly equipped to deal with the noise, loss, and uncertainty in data. We discuss a suite of techniques based on probabilistic models that are designed to allow database to tolerate noise ..."
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Cited by 52 (3 self)
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Traditional database systems, particularly those focused on capturing and managing data from the real world, are poorly equipped to deal with the noise, loss, and uncertainty in data. We discuss a suite of techniques based on probabilistic models that are designed to allow database to tolerate noise and loss. These techniques are based on exploiting correlations to predict missing values and identify outliers. Interestingly, correlations also provide a way to give approximate answers to users at a significantly lower cost and enable a range of new types of queries over the correlation structure itself. We illustrate a host of applications for our new techniques and queries, ranging from sensor networks to network monitoring to data stream management. We also present a unified architecture for integrating such models into database systems, focusing in particular on acquisitional systems where the cost of capturing data (e.g., from sensors) is itself a significant part of the query processing cost.
Feature Bagging for Outlier Detection
, 2005
"... Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel feature bagging approach for detecting outliers in very large, high dimensional and noisy databases is proposed. It combines results from multiple outlier detection algori ..."
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Cited by 48 (2 self)
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Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel feature bagging approach for detecting outliers in very large, high dimensional and noisy databases is proposed. It combines results from multiple outlier detection algorithms that are applied using different set of features. Every outlier detection algorithm uses a small subset of features that are randomly selected from the original feature set. As a result, each outlier detector identifies different outliers, and thus assigns to all data records outlier scores that correspond to their probability of being outliers. The outlier scores computed by the individual outlier detection algorithms are then combined in order to find the better quality outliers. Experiments performed on several synthetic and real life data sets show that the proposed methods for combining outputs from multiple outlier detection algorithms provide nontrivial improvements over the base algorithm.
Trajectory Outlier Detection: A PartitionandDetect Framework
"... Abstract — Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying subtrajectories. In this paper, we propose a ..."
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Cited by 43 (9 self)
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Abstract — Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying subtrajectories. In this paper, we propose a novel partitionanddetect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying subtrajectories from a trajectory database. Based on this partitionanddetect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a twolevel trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distancebased and densitybased approaches. Experimental results demonstrate that TRAOD correctly detects outlying subtrajectories from real trajectory data. I.