Deep Learning for Anomaly Detection: a Review
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A Review on Outlier/Anomaly Detection in Time Series Data
A review on outlier/anomaly detection in time series data ANE BLÁZQUEZ-GARCÍA and ANGEL CONDE, Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Spain USUE MORI, Intelligent Systems Group (ISG), Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Spain JOSE A. LOZANO, Intelligent Systems Group (ISG), Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Spain and Basque Center for Applied Mathematics (BCAM), Spain Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique. Additional Key Words and Phrases: Outlier detection, anomaly detection, time series, data mining, taxonomy, software 1 INTRODUCTION Recent advances in technology allow us to collect a large amount of data over time in diverse research areas. Observations that have been recorded in an orderly fashion and which are correlated in time constitute a time series. Time series data mining aims to extract all meaningful knowledge from this data, and several mining tasks (e.g., classification, clustering, forecasting, and outlier detection) have been considered in the literature [Esling and Agon 2012; Fu 2011; Ratanamahatana et al. -
Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
applied sciences Article Image Anomaly Detection Using Normal Data Only by Latent Space Resampling Lu Wang 1 , Dongkai Zhang 1 , Jiahao Guo 1 and Yuexing Han 1,2,* 1 School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; [email protected] (L.W.); [email protected] (D.Z.); [email protected] (J.G.) 2 Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai 200444, China * Correspondence: [email protected] Received: 29 September 2020; Accepted: 30 November 2020; Published: 3 December 2020 Abstract: Detecting image anomalies automatically in industrial scenarios can improve economic efficiency, but the scarcity of anomalous samples increases the challenge of the task. Recently, autoencoder has been widely used in image anomaly detection without using anomalous images during training. However, it is hard to determine the proper dimensionality of the latent space, and it often leads to unwanted reconstructions of the anomalous parts. To solve this problem, we propose a novel method based on the autoencoder. In this method, the latent space of the autoencoder is estimated using a discrete probability model. With the estimated probability model, the anomalous components in the latent space can be well excluded and undesirable reconstruction of the anomalous parts can be avoided. Specifically, we first adopt VQ-VAE as the reconstruction model to get a discrete latent space of normal samples. Then, PixelSail, a deep autoregressive model, is used to estimate the probability model of the discrete latent space. In the detection stage, the autoregressive model will determine the parts that deviate from the normal distribution in the input latent space. -
Unsupervised Network Anomaly Detection Johan Mazel
Unsupervised network anomaly detection Johan Mazel To cite this version: Johan Mazel. Unsupervised network anomaly detection. Networking and Internet Architecture [cs.NI]. INSA de Toulouse, 2011. English. tel-00667654 HAL Id: tel-00667654 https://tel.archives-ouvertes.fr/tel-00667654 Submitted on 8 Feb 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 5)µ4& &OWVFEFMPCUFOUJPOEV %0$503"5%&-6/*7&34*5²%&506-064& %ÏMJWSÏQBS Institut National des Sciences Appliquées de Toulouse (INSA de Toulouse) $PUVUFMMFJOUFSOBUJPOBMFBWFD 1SÏTFOUÏFFUTPVUFOVFQBS Mazel Johan -F lundi 19 décembre 2011 5J tre : Unsupervised network anomaly detection ED MITT : Domaine STIC : Réseaux, Télécoms, Systèmes et Architecture 6OJUÏEFSFDIFSDIF LAAS-CNRS %JSFDUFVS T EFʾÒTF Owezarski Philippe Labit Yann 3BQQPSUFVST Festor Olivier Leduc Guy Fukuda Kensuke "VUSF T NFNCSF T EVKVSZ Chassot Christophe Vaton Sandrine Acknowledgements I would like to first thank my PhD advisors for their help, support and for letting me lead my research toward my own direction. Their inputs and comments along the stages of this thesis have been highly valuable. I want to especially thank them for having been able to dive into my work without drowning, and then, provide me useful remarks. -
Outlier Detection in Graphs: a Study on the Impact of Multiple Graph Models
Computer Science and Information Systems 16(2):565–595 https://doi.org/10.2298/CSIS181001010C Outlier Detection in Graphs: A Study on the Impact of Multiple Graph Models Guilherme Oliveira Campos1;2, Edre´ Moreira1, Wagner Meira Jr.1, and Arthur Zimek2 1 Federal University of Minas Gerais Belo Horizonte, Minas Gerais, Brazil fgocampos,edre,[email protected] 2 University of Southern Denmark Odense, Denmark [email protected] Abstract. Several previous works proposed techniques to detect outliers in graph data. Usually, some complex dataset is modeled as a graph and a technique for de- tecting outliers in graphs is applied. The impact of the graph model on the outlier detection capabilities of any method has been ignored. Here we assess the impact of the graph model on the outlier detection performance and the gains that may be achieved by using multiple graph models and combining the results obtained by these models. We show that assessing the similarity between graphs may be a guid- ance to determine effective combinations, as less similar graphs are complementary with respect to outlier information they provide and lead to better outlier detection. Keywords: outlier detection, multiple graph models, ensemble. 1. Introduction Outlier detection is a challenging problem, since the concept of outlier is problem-de- pendent and it is hard to capture the relevant dimensions in a single metric. The inherent subjectivity related to this task just intensifies its degree of difficulty. The increasing com- plexity of the datasets as well as the fact that we are deriving new datasets through the integration of existing ones is creating even more complex datasets. -
Machine Learning and Extremes for Anomaly Detection — Apprentissage Automatique Et Extrêmes Pour La Détection D’Anomalies
École Doctorale ED130 “Informatique, télécommunications et électronique de Paris” Machine Learning and Extremes for Anomaly Detection — Apprentissage Automatique et Extrêmes pour la Détection d’Anomalies Thèse pour obtenir le grade de docteur délivré par TELECOM PARISTECH Spécialité “Signal et Images” présentée et soutenue publiquement par Nicolas GOIX le 28 Novembre 2016 LTCI, CNRS, Télécom ParisTech, Université Paris-Saclay, 75013, Paris, France Jury : Gérard Biau Professeur, Université Pierre et Marie Curie Examinateur Stéphane Boucheron Professeur, Université Paris Diderot Rapporteur Stéphan Clémençon Professeur, Télécom ParisTech Directeur Stéphane Girard Directeur de Recherche, Inria Grenoble Rhône-Alpes Rapporteur Alexandre Gramfort Maitre de Conférence, Télécom ParisTech Examinateur Anne Sabourin Maitre de Conférence, Télécom ParisTech Co-directeur Jean-Philippe Vert Directeur de Recherche, Mines ParisTech Examinateur List of Contributions Journal Sparse Representation of Multivariate Extremes with Applications to Anomaly Detection. (Un- • der review for Journal of Multivariate Analysis). Authors: Goix, Sabourin, and Clémençon. Conferences On Anomaly Ranking and Excess-Mass Curves. (AISTATS 2015). • Authors: Goix, Sabourin, and Clémençon. Learning the dependence structure of rare events: a non-asymptotic study. (COLT 2015). • Authors: Goix, Sabourin, and Clémençon. Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking. (AIS- • TATS 2016). Authors: Goix, Sabourin, and Clémençon. How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms? (to be submit- • ted). Authors: Goix and Thomas. One-Class Splitting Criteria for Random Forests with Application to Anomaly Detection. (to be • submitted). Authors: Goix, Brault, Drougard and Chiapino. Workshops Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking. (NIPS • 2015 Workshop on Nonparametric Methods for Large Scale Representation Learning). Authors: Goix, Sabourin, and Clémençon. -
Machine Learning for Anomaly Detection and Categorization In
Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments Tara Salman Deval Bhamare Aiman Erbad Raj Jain Mohammed SamaKa Washington University in St. Louis, Qatar University, Qatar University, Washington University in St. Louis, Qatar University, St. Louis, USA Doha, Qatar Doha, Qatar St. Louis, USA Doha, Qatar [email protected] [email protected] [email protected] [email protected] [email protected] Abstract— Cloud computing has been widely adopted by is getting popular within organizations, clients, and service application service providers (ASPs) and enterprises to reduce providers [2]. Despite this fact, data security is a major concern both capital expenditures (CAPEX) and operational expenditures for the end-users in multi-cloud environments. Protecting such (OPEX). Applications and services previously running on private environments against attacks and intrusions is a major concern data centers are now being migrated to private or public clouds. in both research and industry [3]. Since most of the ASPs and enterprises have globally distributed user bases, their services need to be distributed across multiple Firewalls and other rule-based security approaches have clouds, spread across the globe which can achieve better been used extensively to provide protection against attacks in performance in terms of latency, scalability and load balancing. the data centers and contemporary networks. However, large The shift has eventually led the research community to study distributed multi-cloud environments would require a multi-cloud environments. However, the widespread acceptance significantly large number of complicated rules to be of such environments has been hampered by major security configured, which could be costly, time-consuming and error concerns. -
CSE601 Anomaly Detection
Anomaly Detection Jing Gao SUNY Buffalo 1 Anomaly Detection • Anomalies – the set of objects are considerably dissimilar from the remainder of the data – occur relatively infrequently – when they do occur, their consequences can be quite dramatic and quite often in a negative sense “Mining needle in a haystack. So much hay and so little time” 2 Definition of Anomalies • Anomaly is a pattern in the data that does not conform to the expected behavior • Also referred to as outliers, exceptions, peculiarities, surprise, etc. • Anomalies translate to significant (often critical) real life entities – Cyber intrusions – Credit card fraud 3 Real World Anomalies • Credit Card Fraud – An abnormally high purchase made on a credit card • Cyber Intrusions – Computer virus spread over Internet 4 Simple Example Y • N1 and N2 are N regions of normal 1 o1 behavior O3 • Points o1 and o2 are anomalies o2 • Points in region O 3 N2 are anomalies X 5 Related problems • Rare Class Mining • Chance discovery • Novelty Detection • Exception Mining • Noise Removal 6 Key Challenges • Defining a representative normal region is challenging • The boundary between normal and outlying behavior is often not precise • The exact notion of an outlier is different for different application domains • Limited availability of labeled data for training/validation • Malicious adversaries • Data might contain noise • Normal behaviour keeps evolving 7 Aspects of Anomaly Detection Problem • Nature of input data • Availability of supervision • Type of anomaly: point, contextual, structural -
Anomaly Detection in Data Mining: a Review Jagruti D
Volume 7, Issue 4, April 2017 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Anomaly Detection in Data Mining: A Review Jagruti D. Parmar Prof. Jalpa T. Patel M.E IT, SVMIT, Bharuch, Gujarat, CSE & IT, SVMIT Bharuch, Gujarat, India India Abstract— Anomaly detection is the new research topic to this new generation researcher in present time. Anomaly detection is a domain i.e., the key for the upcoming data mining. The term ‘data mining’ is referred for methods and algorithms that allow extracting and analyzing data so that find rules and patterns describing the characteristic properties of the information. Techniques of data mining can be applied to any type of data to learn more about hidden structures and connections. In the present world, vast amounts of data are kept and transported from one location to another. The data when transported or kept is informed exposed to attack. Though many techniques or applications are available to secure data, ambiguities exist. As a result to analyze data and to determine different type of attack data mining techniques have occurred to make it less open to attack. Anomaly detection is used the techniques of data mining to detect the surprising or unexpected behaviour hidden within data growing the chances of being intruded or attacked. This paper work focuses on Anomaly Detection in Data mining. The main goal is to detect the anomaly in time series data using machine learning techniques. Keywords— Anomaly Detection; Data Mining; Time Series Data; Machine Learning Techniques. -
Anomaly Detection in Raw Audio Using Deep Autoregressive Networks
ANOMALY DETECTION IN RAW AUDIO USING DEEP AUTOREGRESSIVE NETWORKS Ellen Rushe, Brian Mac Namee Insight Centre for Data Analytics, University College Dublin ABSTRACT difficulty to parallelize backpropagation though time, which Anomaly detection involves the recognition of patterns out- can slow training, especially over very long sequences. This side of what is considered normal, given a certain set of input drawback has given rise to convolutional autoregressive ar- data. This presents a unique set of challenges for machine chitectures [24]. These models are highly parallelizable in learning, particularly if we assume a semi-supervised sce- the training phase, meaning that larger receptive fields can nario in which anomalous patterns are unavailable at training be utilised and computation made more tractable due to ef- time meaning algorithms must rely on non-anomalous data fective resource utilization. In this paper we adapt WaveNet alone. Anomaly detection in time series adds an additional [24], a robust convolutional autoregressive model originally level of complexity given the contextual nature of anomalies. created for raw audio generation, for anomaly detection in For time series modelling, autoregressive deep learning archi- audio. In experiments using multiple datasets we find that we tectures such as WaveNet have proven to be powerful gener- obtain significant performance gains over deep convolutional ative models, specifically in the field of speech synthesis. In autoencoders. this paper, we propose to extend the use of this type of ar- The remainder of this paper proceeds as follows: Sec- chitecture to anomaly detection in raw audio. In experiments tion 2 surveys recent related work on the use of deep neu- using multiple audio datasets we compare the performance of ral networks for anomaly detection; Section 3 describes the this approach to a baseline autoencoder model and show su- WaveNet architecture and how it has been re-purposed for perior performance in almost all cases. -
Detecting Anomalies in System Log Files Using Machine Learning Techniques
Institute of Software Technology University of Stuttgart Universitätsstraße 38 D–70569 Stuttgart Bachelor’s Thesis 148 Detecting Anomalies in System Log Files using Machine Learning Techniques Tim Zwietasch Course of Study: Computer Science Examiner: Prof. Dr. Lars Grunske Supervisor: M. Sc. Teerat Pitakrat Commenced: 2014/05/28 Completed: 2014/10/02 CR-Classification: B.8.1 Abstract Log files, which are produced in almost all larger computer systems today, contain highly valuable information about the health and behavior of the system and thus they are consulted very often in order to analyze behavioral aspects of the system. Because of the very high number of log entries produced in some systems, it is however extremely difficult to find relevant information in these files. Computer-based log analysis techniques are therefore indispensable for the process of finding relevant data in log files. However, a big problem in finding important events in log files is, that one single event without any context does not always provide enough information to detect the cause of the error, nor enough information to be detected by simple algorithms like the search with regular expressions. In this work, three different data representations for textual information are developed and evaluated, which focus on the contextual relationship between the data in the input. A new position-based anomaly detection algorithm is implemented and compared to various existing algorithms based on the three new representations. The algorithms are executed on a semantically filtered set of a labeled BlueGene/L log file and evaluated by analyzing the correlation between the labels contained in the log file and the anomalous events created by the algorithms. -
Anomaly Detection with Adversarial Dual Autoencoders
Anomaly Detection with Adversarial Dual Autoencoders Vu Ha Son1, Ueta Daisuke2, Hashimoto Kiyoshi2, Maeno Kazuki3, Sugiri Pranata1, Sheng Mei Shen1 1Panasonic R&D Center Singapore {hason.vu,sugiri.pranata,shengmei.shen}@sg.panasonic.com 2Panasonic CNS – Innovation Center 3Panasonic CETDC {ueta.daisuke,hashimoto.kiyoshi,maeno.kazuki}@jp.panasonic.com Abstract Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. However, GAN training is somewhat challenging and unstable. Inspired from previous work in GAN-based image generation, we introduce a GAN-based anomaly detection framework – Adversarial Dual Autoencoders (ADAE) - consists of two autoencoders as generator and discriminator to increase training stability. We also employ discriminator reconstruction error as anomaly score for better detection performance. Experiments across different datasets of varying complexity show strong evidence of a robust model that can be used in different scenarios, one of which is brain tumor detection. Keywords: Anomaly Detection, Generative Adversarial Networks, Brain Tumor Detection 1 Introduction The task of anomaly detection is informally defined as follows: given the set of normal behaviors, one must detect whether incoming input exhibits any irregularity. In anomaly detection, semi-supervised and unsupervised approaches have been dominant recently, as the weakness of supervised approaches is that they require monumental effort in labeling data. On the contrary, semi-supervised and unsupervised methods do not require many data labeling, making them desirable, especially for rare/unseen anomalous cases. Out of the common methods for semi and unsupervised anomaly detection such as variational autoencoder (VAE), autoencoder (AE) and GAN, GAN-based methods are among the most popular choices. -
Comparison of Data Mining Methods on Different Applications: Clustering and Classification Methods
Inf. Sci. Lett. 4, No. 2, 61-66 (2015) 61 Information Sciences Letters An International Journal http://dx.doi.org/10.12785/isl/040202 Comparison of Data Mining Methods on Different Applications: Clustering and Classification Methods Richard Merrell∗ and David Diaz Department of Computer Engineering, Yerevan University, Yerevan, Armenia. Received: 12 Jan. 2015, Revised: 21 Apr. 2015, Accepted: 24 Apr. 2015 Published online: 1 May 2015 Abstract: Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. In this review we study different type if clustering methods. Keywords: Clustering, Data Mining, Ensemble, Big data. 1 Introduction connected by an edge can be considered as a prototypical form of cluster. Relaxations of the complete connectivity According to Vladimir Estivill-Castro, the notion of a requirement (a fraction of the edges can be missing) are ”cluster” cannot be precisely defined, which is one of the known as quasi-cliques. A ”clustering” is essentially a set reasons why there are so many clustering algorithms [1,2, of such clusters, usually containing all objects in the data 3,4]. There is a common denominator: a group of data set. Additionally, it may specify the relationship of the objects.