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Anomalies are patterns in data that
Anomalies are patterns in data that do not conform to a well defined notion of normal
behavior. The problem of finding these patterns is referred to as anomaly detection.
The importance of anomaly detection is due to the fact that anomalies in data translate
to significant and actionable information in a wide variety of application domains (1).
For example, an anomalous traffic pattern in a computer network could mean that a
hacked computer is sending out sensitive data to an unauthorized destination (2). An
anomalous MRI image may indicate the presence of malignant tumors (3) or anomalies
in credit card transaction data could indicate credit card or identity theft (4) . Detecting
anomalies has been studied by several research communities to address issues in different application domains (1).
Anomalies are patterns in data that do not conform to a well defined notion of normal
behavior. The problem of finding these patterns is referred to as anomaly detection.
The importance of anomaly detection is due to the fact that anomalies in data translate
to significant and actionable information in a wide variety of application domains (1).
For example, an anomalous traffic pattern in a computer network could mean that a
hacked computer is sending out sensitive data to an unauthorized destination (2). An
anomalous MRI image may indicate the presence of malignant tumors (3) or anomalies
in credit card transaction data could indicate credit card or identity theft (4) . Detecting
anomalies has been studied by several research communities to address issues in different
application domains (1).
behavior. The problem of finding these patterns is referred to as anomaly detection.
The importance of anomaly detection is due to the fact that anomalies in data translate
to significant and actionable information in a wide variety of application domains (1).
For example, an anomalous traffic pattern in a computer network could mean that a
hacked computer is sending out sensitive data to an unauthorized destination (2). An
anomalous MRI image may indicate the presence of malignant tumors (3) or anomalies
in credit card transaction data could indicate credit card or identity theft (4) . Detecting
anomalies has been studied by several research communities to address issues in different application domains (1).
Anomalies are patterns in data that do not conform to a well defined notion of normal
behavior. The problem of finding these patterns is referred to as anomaly detection.
The importance of anomaly detection is due to the fact that anomalies in data translate
to significant and actionable information in a wide variety of application domains (1).
For example, an anomalous traffic pattern in a computer network could mean that a
hacked computer is sending out sensitive data to an unauthorized destination (2). An
anomalous MRI image may indicate the presence of malignant tumors (3) or anomalies
in credit card transaction data could indicate credit card or identity theft (4) . Detecting
anomalies has been studied by several research communities to address issues in different
application domains (1).
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Anomalies are patterns in data that do not conform to a well defined notion of normalbehavior. The problem of finding these patterns is referred to as anomaly detection.The importance of anomaly detection is due to the fact that anomalies in data translateto significant and actionable information in a wide variety of application domains (1).For example, an anomalous traffic pattern in a computer network could mean that ahacked computer is sending out sensitive data to an unauthorized destination (2). Ananomalous MRI image may indicate the presence of malignant tumors (3) or anomaliesin credit card transaction data could indicate credit card or identity theft (4) . Detectinganomalies has been studied by several research communities to address issues in different application domains (1).Anomalies are patterns in data that do not conform to a well defined notion of normalbehavior. The problem of finding these patterns is referred to as anomaly detection.The importance of anomaly detection is due to the fact that anomalies in data translateto significant and actionable information in a wide variety of application domains (1).For example, an anomalous traffic pattern in a computer network could mean that ahacked computer is sending out sensitive data to an unauthorized destination (2). Ananomalous MRI image may indicate the presence of malignant tumors (3) or anomaliesin credit card transaction data could indicate credit card or identity theft (4) . Detectinganomalies has been studied by several research communities to address issues in differentapplication domains (1).
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Anomalies are patterns in data that do not conform to a well defined notion of normal
behavior. The problem of finding these patterns is referred to as anomaly detection.
The importance of anomaly detection is due to the fact that anomalies in data translate
to significant and actionable information in a wide variety of application domains (1).
For example, an anomalous traffic pattern in a computer network could mean that a
hacked computer is sending out sensitive data to an unauthorized destination (2). An
anomalous MRI image may indicate the presence of malignant tumors (3) or anomalies
in credit card transaction data could indicate credit card or identity theft (4) . Detecting
anomalies has been studied by several research communities to address issues in different application domains (1).
Anomalies are patterns in data that do not conform to a well defined notion of normal
behavior. The problem of finding these patterns is referred to as anomaly detection.
The importance of anomaly detection is due to the fact that anomalies in data translate
to significant and actionable information in a wide variety of application domains (1).
For example, an anomalous traffic pattern in a computer network could mean that a
hacked computer is sending out sensitive data to an unauthorized destination (2). An
anomalous MRI image may indicate the presence of malignant tumors (3) or anomalies
in credit card transaction data could indicate credit card or identity theft (4) . Detecting
anomalies has been studied by several research communities to address issues in different
application domains (1).
behavior. The problem of finding these patterns is referred to as anomaly detection.
The importance of anomaly detection is due to the fact that anomalies in data translate
to significant and actionable information in a wide variety of application domains (1).
For example, an anomalous traffic pattern in a computer network could mean that a
hacked computer is sending out sensitive data to an unauthorized destination (2). An
anomalous MRI image may indicate the presence of malignant tumors (3) or anomalies
in credit card transaction data could indicate credit card or identity theft (4) . Detecting
anomalies has been studied by several research communities to address issues in different application domains (1).
Anomalies are patterns in data that do not conform to a well defined notion of normal
behavior. The problem of finding these patterns is referred to as anomaly detection.
The importance of anomaly detection is due to the fact that anomalies in data translate
to significant and actionable information in a wide variety of application domains (1).
For example, an anomalous traffic pattern in a computer network could mean that a
hacked computer is sending out sensitive data to an unauthorized destination (2). An
anomalous MRI image may indicate the presence of malignant tumors (3) or anomalies
in credit card transaction data could indicate credit card or identity theft (4) . Detecting
anomalies has been studied by several research communities to address issues in different
application domains (1).
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