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ACM Computing Surveys, 2009
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.
Varun Chandola, Arindam Banerjee
exaly +3 more sources
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.
Varun Chandola, Arindam Banerjee
exaly +3 more sources
2015
Anomaly detection is the process of finding outliers in the data set. Outliers are the data objects that stand out amongst other objects in the data set and do not conform to the normal behavior in a data set. Anomaly detection is a data mining application that combines multiple data mining tasks like classification, regression, and clustering.
Vijay Kotu, Bala Deshpande
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Anomaly detection is the process of finding outliers in the data set. Outliers are the data objects that stand out amongst other objects in the data set and do not conform to the normal behavior in a data set. Anomaly detection is a data mining application that combines multiple data mining tasks like classification, regression, and clustering.
Vijay Kotu, Bala Deshpande
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A Methodological Overview on Anomaly Detection
2013In this Chapter we give an overview of statistical methods for anomaly detection (AD), thereby targeting an audience of practitioners with general knowledge of statistics. We focus on the applicability of the methods by stating and comparing the conditions in which they can be applied and by discussing the parameters that need to be set.
CALLEGARI, CHRISTIAN +9 more
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Anomaly detection in diurnal data
Computer Networks, 2014In this paper we present methodological advances in anomaly detection tailored to discover abnormal traffic patterns under the presence of seasonal trends in data. In our setup we impose specific assumptions on the traffic type and nature; our study features VoIP call counts, for which several traces of real data has been used in this study, but the ...
Felipe Mata +4 more
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2006
Publisher Summary This chapter discusses how to detect anomalies in code coverage and anomalies in data accesses. It also demonstrates how to infer invariants from multiple test runs automatically, in order to flag later invariant violations. One of the simplest methods for detecting anomalies operates per the following logic: every failure is caused ...
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Publisher Summary This chapter discusses how to detect anomalies in code coverage and anomalies in data accesses. It also demonstrates how to infer invariants from multiple test runs automatically, in order to flag later invariant violations. One of the simplest methods for detecting anomalies operates per the following logic: every failure is caused ...
openaire +3 more sources
Sparse Coding with Anomaly Detection
Journal of Signal Processing Systems, 2013We consider the problem of simultaneous sparse coding and anomaly detection in a collection of data vectors. The majority of the data vectors are assumed to conform with a sparse representation model, whereas the anomaly is caused by an unknown subset of the data vectors - the outliers - which significantly deviate from this model.
Amir Adler +3 more
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2019
Considerable attention has been given to the vulnerability of machine learning to adversarial samples. This is particularly critical in anomaly detection; uses such as detecting fraud, intrusion, and malware must assume a malicious adversary. We specifically address poisoning attacks, where the adversary injects carefully crafted benign samples into ...
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Considerable attention has been given to the vulnerability of machine learning to adversarial samples. This is particularly critical in anomaly detection; uses such as detecting fraud, intrusion, and malware must assume a malicious adversary. We specifically address poisoning attacks, where the adversary injects carefully crafted benign samples into ...
openaire +2 more sources
MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM), 2017
Anomaly detection is the process of identifying unusual signals in a set of observations. This is a vital task in a variety of fields including cybersecurity and the battlefield. In many scenarios, observations are gathered from a set of distributed mobile or small form factor devices.
Alex Lu, Joseph Schneible
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Anomaly detection is the process of identifying unusual signals in a set of observations. This is a vital task in a variety of fields including cybersecurity and the battlefield. In many scenarios, observations are gathered from a set of distributed mobile or small form factor devices.
Alex Lu, Joseph Schneible
openaire +2 more sources
Detecting Anomalies and Intruders [PDF]
, 2006 Brittleness is a well-known problem in expert systems where a conclusion can be made, which human common sense would recognise as impossible e.g. that a male is pregnant. We have extended previous work on prudent expert systems to enable an expert system to recognise when a case is outside its range of experience.
Akara Prayote, Paul Compton
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