Information theory studies the quantification, storage, and communication of information. Detecting network attacks in the internet via statistical. Apart from the vi which possesses a fairly comprehensive characterization, less is known about the mutual information and various forms of the socalled normalized mutual information strehl and ghosh, 2002. Anomaly detection, feature selection, clustering and classification. Investigating deep learning for collective anomaly. Once the sketches have been constructed, they are passed in input to the block that is responsible for the actual anomaly detection phase. Informationtheoretic framework for network anomaly. Anomaly detection, pattern detection, bayesian network, biosurveillance. Informationtheoretic analysis of xray scatter and phase. In theory, every clustering algorithm can be used to cluster the data in a first step. Time series contextual anomaly detection for detecting stock market manipulation by seyed koosha golmohammadi a thesis submitted in partial ful llment of the requirements for the degree of doctor of philosophy. Anomaly detection, information theory, shannon entropy, tsallis entropy, renyi entropy, kullbackleibler divergence, jensenshannon divergence, mawilab.
Towards an informationtheoretic framework for analyzing. Anomaly detection is an essential component of protection mechanisms against novel attacks. This paper presents an anomaly detection module that uses information theoretic measures to generate a fault indicator from a particlefilteringbased estimate of the posterior state pdf of a dynamic system. Here, we propose a new approach to detect outliers in streaming univariate time series based on extreme value. These information theoretic models explore the spacedeviation tradeoff. An informationtheoretic method for the detection of. It was originally proposed by claude shannon in 1948 to find fundamental limits on signal processing and communication operations such as data compression, in a landmark paper titled a mathematical theory of communication. Hierarchical temporal memory htm is a biologically inspired machine intelligence technology that mimics the architecture and processes of the neocortex. Pdf an informationtheoretic combining method for multi. Contents list offigures xv list oftables xvii preface xix acknowledgments xxi abstract xxiii. Anomaly detection refers to the problem of finding patterns in data that do not conform.
We advocate that, in order to separate the malicious feature instances from large volumes of benign and closetobenign feature instances, the feature space of a statistical ads should be sliced into multiple subspaces before anomaly detection is performed. We propose to use several information theoretic measures, namely, entropy, conditional entropy, relative. This challenge is known as unsupervised anomaly detection and is addressed in. Deep approaches to anomaly detection have recently shown. An information theoretic view of intrusion detection cont. Key components associated with an anomaly detection technique. Figure 2 shows the key components associated with any anomaly detection technique. Anomaly detection in streams with extreme value theory. We further introduce an information theoretic framework for deep anomaly detection based on the idea that the entropy of the latent distribution for normal data should be lower than the entropy of. An informationtheoretic combining method for multi. Entropy conditional entropy relative conditional entropy information gain case studies on sendmail system call data were provided to show how to use the information theoretic measures to build anomaly detection models. Anomaly detection is an essential component of the protection mechanisms against novel attacks. Almost all the approaches so far proposed for dos denial of service attack detection with the aid of collective anomaly detection are.
For a survey of anomaly detection problems and current approaches, see 4. Statistical analysis of nearest neighbor methods for anomaly. This book is newer, longer, and more advanced than the previous offering, but it is also a logical next step. This stems from the outsized role anomalies can play in potentially skewing the analysis of data and the subsequent decision making process. Goa first combines multiple wellknown fs methods to yield possible. An entropybased network anomaly detection method mdpi. These measures can be used to describe the characteristics of an audit data set, suggest the appropriate anomaly. Other examples include the cumulative sumcusum algorithm 17, the exponentially weighted moving.
An informationtheoretic approach to detecting changes in. In this white paper we first give an overview of htm as applied to anomaly detection, and then discuss the advantages of an. Towards an informationtheoretic framework for analyzing intrusion detection systems guofei gu1, prahlad fogla1, david dagon1, wenke lee1 and boris skoric2 1 georgia institute of technology, u. This course is an overview of anomaly detection s history, applications, and stateof. Outlier detection techniques, acm sigkdd, 2010, 34, pdf. In data mining, anomaly detection also outlier detection is the identification of rare items. Its impact has been crucial to the success of the voyager missions to deep space. Science of anomaly detection v4 updated for htm for it.
Automatic clustering based on an informationtheoretic. Conference paper pdf available january 2010 with 84 reads how we measure reads. Most current approaches make judgments based on the. Information theoretic measures for clusterings comparison. Introduction the seminal darpa ids evaluation of 1999 emphasized and catalyzed a shift in focus from signaturebased intrusion detection to anomaly detection which can detect zeroday previouslyunknown attacks 1. A gradientbased explainable variational autoencoder. A new instance which lies in the low probability area of this pdf is declared. Improving anomaly detection performance using information theoretic and machine learning tools.
Pdf informationtheoretic measures for anomaly detection. An informationtheoretic approach to detecting changes in multidimensional data streams. Anomaly detection using an ensemble of feature models. An informationtheoretic combining method for multiclassifier anomaly detection systems. In particular, we focus on providing an experimental evaluation of anomaly detectors based on entropy. Time series contextual anomaly detection for detecting. Informationtheoretic measures for anomaly detection.
A space shuttle main engine application author 1 1, author 2 2 1 school 1 2 school 2 abstract automated modelfree anomaly and fault detection using large collections of sensor suites is vi. Carlotto automatic clustering based on an informationtheoretic approach with application to spectral anomaly detection. A comparative evaluation of outlier detection algorithms eurecom. An information theoretic measure for anomaly detection in complex dynamical systems. One way to address the above challenges is to apply statistical models and machine learning algorithms. Using an information theoretic perspective on anomaly detection, we derive a loss motivated by the idea that the entropy of the latent distribution for normal data should be. This book covering machine learning is written by shai shalevshwartz and shai bendavid. Deep sad, an endtoend deep methodology for general semisupervised anomaly detection. This paper explores the effectiveness of deep learning and other supervised learning algorithms for collective anomaly detection. Orchard 1, benjamin olivares, matias cerda 1 and jorge f.
Abstract an anomaly is an observation that does not conform to the expected normal behavior. Anomaly detection based on informationtheoretic measures and particle filtering algorithms marcos e. Information theoretic measures for anomaly detection security and priv acy, 2001. Gehm, yuzhang lin, liangchih huang, and amit ashok informationtheoretic analysis of xray scatter and phase architectures for anomaly detection, proc.
Anomaly detection plays a key role in todays world of datadriven decision making. Syed ali khayam anomaly detection systems adss were proposed more than two decades ago and since then considerable research e. This chapter introduces theoretic fundamentals of entropy. Ids research still needs to strengthen mathematical foundations and. Information theoretic point of view, we should have. Hodge and austin 2004 provide an extensive survey of anomaly detection techniques developed in machine learning and statistical domains. One strong line of research that has emerged is rooted in information theory. Attacks to invehicle networks were simulated by injecting different classes of forged can messages in traces captured from a modern licensed. This paper evaluates the effectiveness of information theoretic anomaly detection algorithms applied to networks included in modern vehicles. Network security, distributed denial of service, ddos, dos, anomaly detection, intrusion detection, attack source identi cation, information theory, statistical.
A comparative evaluation of unsupervised anomaly detection. Evaluation of anomaly detection for invehicle networks. From an information theoretic point of view, we should. Anomaly detection based on informationtheoretic measures. In this paper, some informationtheoretic measures for anomaly detection have been proposed. Reichl, how to increase security in mobile networks by anomaly detection, proceedings of the 14th annual ieee computer security applications conference, pp. This paper consolidates and enhances this concept to build a rigorous theory based on thermodynamic formalism of complex systems for anomaly detection. The study of the theoretical foundations of deep learning is an active and. We propose to use several information theoretic measures, namely, entropy, conditional entropy, relative conditional entropy, information gain, and information cost for anomaly detection. Anomaly detection related books, papers, videos, and toolboxes. An information theoretic approach guofei gu, prahlad fogla, david dagon.
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