Objectcentric anomaly detection by attributebased reasoning. Application in anomaly detection approach for anomaly detection using the information theoretic measures 1. Kolmogorov complexity, entropy, relative entropy, etc. Its main advantages are that it is distributable, local, and tunable. A main reason for this limitation is the expectation that an ads should achieve very high accuracy while having extremely low computational complexity. This approach is both generalby using generalpurpose measures borrowed from information theory and statisticsand scalablethrough anomaly detection pipelines that are executed in a distributed setting over stateoftheart big. 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.
While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multidimensional points, with graph data becoming ubiquitous, techniques for structured \\em. Entropybased indexing for computational efficiency and robust performance, author tourassi, georgia d and harrawood, brian and singh, swatee and lo, joseph y and digital advanced imaging laboratories, department of radiology, duke university medical center, durham, north carolina 27705, abstractnote we. There are several challenges, however, in monitoring this statistic. Anomaly detection is an essential component of protection mechanisms against novel attacks. Informationtheoretic measures for anomaly detection semantic. A measure for anomaly detection is formulated based on the concepts derived from information theory and statistical thermodynamics. In this paper, we limit our focus of evaluation to measure the e. Information theoretic metrics hold great promise for modeling traffic and detecting anomalies if only they could be computed in an efficient, scalable way. Introduction evaluating intrusion detection systems is a fundamental topic in the. They will allow us to reduce complexity, accelerate query matching times, improve specifcity of the query matches, and incorporate robustness to noise and other. An entropybased network anomaly detection method mdpi. Agentbased cooperative anomaly detection for wireless ad hoc networks. Anomaly sql selectstatement detection using entropy analysis.
A survey of network anomaly detection techniques journal of. Anomaly detection in time series provides significant information for numerous applications. Threats, vulnerabilities, prevention, detection, and management, volume 3 book. Systems and methods for detecting anomalies that are. To mitigate the limitations of relative informationtheoretic measures for the present problem. Informationtheoretic measures for anomaly detection 2001. We propose to use several informationtheoretic measures, namely, entropy, conditional entropy, relative conditional entropy, information gain, and information cost for anomaly detection. Anomaly detection accuracy has been a serious limitation in commercial ads deployments. Distributed monitoring of conditional entropy for network. In this post we briefly discuss proximity based methods and highdimensional outlier detection methods.
Spie 10187, anomaly detection and imaging with xrays adix ii, 1018705 7 june 2017. Information theoretic measures for clusterings comparison. An informationtheoretic method for the detection of. In many applications, data sets may contain thousands of features. Entropybased indexing for computational efficiency and robust performance, author tourassi, georgia d and harrawood, brian and singh, swatee and lo, joseph y and digital advanced imaging laboratories, department of radiology, duke university medical center, durham, north. Information theory, probability and statistics a section. Informationtheoretic measures for anomaly detection wenke lee. A wide variety of technique for anomaly detection such as classification, statistical methods, information theoretic, clustering and spectral approaches has been proposed 1, 12, 20, 22. Pdf evaluation of anomaly detection for invehicle networks. Informationtheoretic measures have been used in many. An informationtheoretic view of intrusion detection cont.
Information theoretic anomaly detection framework for web. The idea of these methods is the fact that outliers increase the minimum code length to describe a data set. Automated feature weighting for network anomaly detection core. Building on the current state of the art in detecting anomalous events 39, 50, 58, the main goal of the paper is to develop a general framework for anomaly detection. They are widely used, such as in bioinformatics, for detecting functionally dependent genes, in marketing, for customer segmentation, in health surveillance, for anomaly detection, and so on.
We propose to use several informationtheoretic measures, namely, entropy, conditional entropy, relative. An informationtheoretic measure for anomaly detection in. Evaluation of anomaly detection for invehicle networks through informationtheoretic algorithms conference paper pdf available september 2016 with 371 reads how we measure reads. In this paper, we propose to use several informationtheoretic measures, namely, entropy, conditional entropy, relative conditional entropy, information gain, and information cost for anomaly detection. This paper presents informationtheoretic analysis of timeseries data to detect slowly evolving anomalies i. Information theory studies the quantification, storage, and communication of information. Informationtheoretic measures for knowledge discovery and. Hodge and austin 2004 provide an extensive survey of anomaly detection techniques developed in machine learning and statistical domains. Y informationtheoretic measures for knowledge discovery and data mining.
Anomaly detection has been greatly revised and expanded. Traffic system anomaly detection using spatiotemporal. Entropy measures, maximum entropy principle and emerging applications. An anomaly detection system based upon principles derived from the immune system was introduced in forr94. Introduction to outlier detection methods data science. Realworld data sets are mostly very high dimensional. Intrusion detection systems basics handbook of information.
Intrusion detection systems basics peng ning, north carolina state university sushil jajodia, george mason university introduction anomaly detection statistical models machine learning and data mining techniques computer immunological approachs specificationbased selection from handbook of information security. Atif j and darbon j copulaset measures on topographic maps for change detection proceedings of the 16th ieee international conference on image processing, 28452848 rungeler m, schotsch b and vary p properties and performance bounds of linear analog block codes proceedings of the 43rd asilomar conference on signals, systems and computers. Finally, the use of various information theoretic measures for anomaly detection is discussed by lee and xiang in. Quantitatively analyzing stealthy communication channels. In this paper, we present a novel, information theoretic anomaly detection framework. In proceedings of the 3rd conference on detection of intrusions and malware, and vulnerability assessment dimva 2006, berlin, germany, july 2006. Xiang, proposed to use some information theoretic measures for anomaly detection. Information theoretic measures form a fundamental class of measures for comparing clusterings, and have recently received increasing interest.
Pdf informationtheoretic measures for anomaly detection. Our countries of survey covers mechanisms of host based invasion sensing system based out of signature based sensing mechanism for known attacks along with anomaly detection techniques for unknown attacks and dos detection denial of service attack. Shannons classic paper a mathematical theory of communication in the bell system technical journal in july and october 1948 prior to this paper, limited informationtheoretic ideas had been developed at bell labs. Intrusion detection systems idss is an important component of the defensein depth or layered network security mechanisms. Built on the concepts of symbolic dynamics, a spatiotemporal pattern network stpn architecture is developed to capture the system characteristics. Automated feature weighting for network anomaly detection. This paper provides an overview of the theoretical, algorithmic and practical developments extending the original proposal. Mathematical foundations and applications in informationtheoretic and statistical problems deadline. The information theoretic approach to signal anomaly. It is difficult to circumscribe the theoretical areas precisely. A data mining framework for building intrusion detection models. Mutual information applied to anomaly detection cse. Networks and network traffic anomalies springer for.
While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multidimensional points, with graph data becoming ubiquitous, techniques for structured \\em graph data have. Creating an experimental testbed for information theoretic analysis of architectures for xray anomaly detection. The application of entropybased anomaly detectors to. Entropy can be used to measure the regularity of an audit dataset of unordered records. Intrusion detection, performance measurement, informationtheoretic 1. Nevertheless, a number of questions concerning their properties and interrelationships remain unresolved. The host based intrusion detection systems information. We address the problem of anomaly detection in machine perception. Received 11 april 2007 received in revised form 30 march 2008 accepted 1 april 2008 available online 2 june 2008 keywords. The methods used for anomaly detection are mostly unsupervised and nonparametric. Mechanical systems and signal processing an information. A computerimplemented method for detecting anomalies that are potentially indicative of malicious attacks, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising. For dnsbased anomaly detection, karasaridis et al described the use of the kullbackleibler distance mentioned in section 4 to measure byte distribution in dns datagrams 11. Instead of statistics, it employs lossless compression for measuring the information quantity, and detects outliers according to compression result.
We the authors are with 1the centre for vision, speech and sig. In spie anomaly detection and imaging with xrays ii, 10187, 1018709101879. The acm s special interest group on algorithms and computation theory sigact provides the. Nonparametric measures take two distributions as input and produce two numbers as output. In this paper, we perform an organized study of information theoretic measures for clustering. However, the computation of information theoretic measures is still based on statistics.
For example, statistical feature extraction and informationtheoretic approaches have been shown to create an anomaly measure for damage detection and tracking in electromechanical systems 32, 33. This paper proposes a systematic data mining technique to detect traffic systemlevel anomalies in a batchprocessing fashion. In more detail, this module is responsible of reading the network traffic e. Intrusion detection with unlabeled data using clustering. Online anomaly detection over big data streams springerlink. In summary, for the present special issue, manuscripts focused on any of the abovementioned information theoretic measures as mutual information, permutation entropy approaches, sample entropy, wavelet entropy and its evaluations, as well as, its interdisciplinaries applications are more than welcome. Information theoretic measures for anomaly detection, 2001. Xiang, d informationtheoretic measures for anomaly detection. Among all algorithms proposed in the literature, this paper assesses the effectiveness of an information theoretic anomaly detector 14, based on the computation of entropy 12. Specifc methods to handle high dimensional sparse data. Informationtheoretic methods for deep learning based data acquisition, analysis and security deadline.
The basic assumption about information theoretic approaches for anomaly detection is that anomalies in the data include irregularities in the information content of the dataset. Informationtheoretic measures for anomaly detection abstract. A computerimplemented method for detecting anomalies that are potentially indicative of malicious attacks may include 1 identifying a sequence of activities performed on a computing device, 2 calculating a cumulative influence score between pairs of activities in the sequence of activities through convolution of the sequence of activities, 3 detecting an. Detecting anomalies in data is a vital task, with numerous highimpact applications in areas such as security, finance, health care, and law enforcement.
Yoann altmann, steve mclaughlin, alfred hero, robust linear spectral unmixing using anomaly detection, arxiv 1501. Anomaly analysis based on metasubspace approach for. Section 7 discusses the dataset issues related to network traffic and section 8 compares and contrasts different categories of network anomaly detection techniques. In this method, the outliers increase the minimum code length to describe a data set. Aug 11, 2017 this undertaking work is about the host based intrusion detection system. For example, it is an anomaly that is a premature ventricular contraction in electrocardiogram ecg signals in fig. The value of his also used to measure the randomness. Informationtheoretic measures, sparse approximation and dimensionality reduction will play key roles in our work.
Informationtheoretic outlier detection for largescale. Theoretical computer science tcs is a subset of general computer science and mathematics that focuses on more mathematical topics of computing and includes the theory of computation. An overview of deep learning based methods for unsupervised. Nonparametric informationtheoretic measures of one. Other than statistical and markovian model, information theory provides a different perspective about anomaly detection. The acms special interest group on algorithms and computation theory sigact provides the following description. We study nonparametric measures for the problem of comparing distributions, which arise in anomaly detection for continuous time series. Dagon 2 proposed to quantify how anomalous the number of queries for each domain name during an hour in a day with chebyshevs inequality and distance measures. Entropy is an informationtheoretic statistic that measures the variability of the feature under consideration. Clustering and outlier detection are two key data mining tasks. A general collection of the technique for the detection of anomalies in. Streaming estimation of informationtheoretic metrics for anomaly detection extended abstract. He obtained his diplom degree in computer science from the university of dortmund, germany, in 1999 and his msc with distinction. This undertaking work is about the host based intrusion detection system.
Evaluation of anomaly detection for invehicle networks. Part of the lecture notes in computer science book series lncs, volume 6640. Lee, et al, informationtheoretic measures for anomaly detection, ieee symposium on security 2001 distance based outlier detection schemes ynearest neighbor nn approach1,2 for each data point d compute the distance to the kth nearest neighbor d k sort all data points according to the distance d k. Informationtheoretic measures for anomaly detection. In this paper, we propose to use several informationtheoretic measures, namely, entropy, conditional entropy, relative conditional entropy, information gain, and information cost, for anomaly detection. Measure regularity of audit data and perform appropriate data transformation iterate this step if necessary so that the dataset used for modeling has high regularity. The landmark event that established the discipline of information theory and brought it to immediate worldwide attention was the publication of claude e. Proceedings of 2001 ieee symposium on security and privacy, 2001 s p 2001. Existing approachesstatistical, nearest neighbordensitybased, and clustering basedhave been retained and updated, while new approaches have been added. Traffic dynamics in the urban interstate system are critical in terms of highway safety and mobility. Part of the lecture notes in computer science book series lncs, volume 8397. A novel approach for anomaly detection in data streams. Theoretical computer science tcs is a subset of general computer science and mathematics that focuses on more mathematical topics of computing and includes the theory of computation it is difficult to circumscribe the theoretical areas precisely.
An anomaly detection algorithm based on lossless compression. Streaming estimation of informationtheoretic metrics for. Built on the concepts of symbolic dynamics, a spatiotemporal pattern network stpn architecture is developed to capture the system. However, the focus of the paper is on determining the suitability of data models through the use of measures such as entropy and relative entropy i. Jun 14, 2019 this approach is both generalby using generalpurpose measures borrowed from information theory and statisticsand scalablethrough anomaly detection pipelines that are executed in a distributed setting over stateoftheart big data streaming and batch processing infrastructures. Anomalous activity in network tra c can be captured by detecting changes in this variability. Creating an experimental testbed for informationtheoretic analysis of architectures for xray anomaly detection. For these techniques to work well, some kind of dependency between the ob. For example, it can be used to detect heart arrhythmia in electrocardiogram, incident faults in industrial process and intrusions in network data. First of all the input data are processed by a module called, in fig. In this paper, we propose an anomaly detection approach that utilizes three measures.
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