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A Multitask Learning framework for E-commerce time series analytics: Operational risk, demand forecasting, and anomaly detection. [PDF]
PLoS OneHe J, An X.
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3D-FPFH-Int: a hybrid geometric-radiometric descriptor for structural surface anomaly detection in tropical heritage. [PDF]
Sci RepGbran H, Murtiono H.
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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
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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.
Varun Chandola, Arindam Banerjee
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An Overview of Anomaly Detection
IT Professional, 2013Security automation continues to depend on signature models, but vulnerability exploitation is exceeding the abilities of such models. The authors, in reviewing the different types of mathematical-based constructs in anomaly detection, reveal how anomaly detection can enhance network security by potentially solving problems that signature models can't ...
Char Sample, Kim Schaffer
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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.
Joseph Schneible, Alex Lu
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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.
Joseph Schneible, Alex Lu
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IEEE Transactions on Knowledge and Data Engineering, 2007
When anomaly detection software is used as a data analysis tool, finding the hardest-to-detect anomalies is not the most critical task. Rather, it is often more important to make sure that those anomalies that are reported to the user are in fact interesting.
Xiuyao Song +3 more
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When anomaly detection software is used as a data analysis tool, finding the hardest-to-detect anomalies is not the most critical task. Rather, it is often more important to make sure that those anomalies that are reported to the user are in fact interesting.
Xiuyao Song +3 more
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2006 IEEE Symposium on Security and Privacy (S&P'06), 2006
Beginning with the work of Forrest et al, several researchers have developed intrusion detection techniques based on modeling program behaviors in terms of system calls. A weakness of these techniques is that they focus on control flows involving system calls, but not their arguments.
Sandeep Bhatkar +2 more
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Beginning with the work of Forrest et al, several researchers have developed intrusion detection techniques based on modeling program behaviors in terms of system calls. A weakness of these techniques is that they focus on control flows involving system calls, but not their arguments.
Sandeep Bhatkar +2 more
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Anomaly detection in trajectories
2016 24th Signal Processing and Communication Application Conference (SIU), 2016In this work, we study the problem of anomaly detection of the trajectories of objects in a visual scene. For this purpose, we propose a novel representation for trajectories utilizing covariance features. Representing trajectories via co-variance features enables us to calculate the distance between the trajectories of different lengths. After setting
Hamza Ergezer, Kemal Leblebicioglu 0001
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2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
Anomaly detection (AD) is designed to find targets that are spectrally distinct from their surrounding neighborhood. Unfortunately, commonly used anomaly detectors generally do not take into account its surrounding spatial information. This paper derives an iterative version of anomaly detection, iterative anomaly detection (IAD) to address this issue.
Yulei Wang 0002 +8 more
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Anomaly detection (AD) is designed to find targets that are spectrally distinct from their surrounding neighborhood. Unfortunately, commonly used anomaly detectors generally do not take into account its surrounding spatial information. This paper derives an iterative version of anomaly detection, iterative anomaly detection (IAD) to address this issue.
Yulei Wang 0002 +8 more
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Anomaly detection for diagnosis
[1990] Digest of Papers. Fault-Tolerant Computing: 20th International Symposium, 2002The author presents a method for detecting anomalous events in communication networks and other similarly characterized environments in which performance anomalies are indicative of failure. The methodology, based on automatically learning the difference between normal and abnormal behavior, has been implemented as part of an automated diagnosis system
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