Sequential anomaly detection
First Claim
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1. A method comprising:
- collecting a first computer process related dataset comprising a plurality of normal temporal event sequences;
learning, using the first computer process related dataset, a one-class sequence classifier f(x) that obtains a decision boundary configured to label temporal event sequences;
collecting a second computer process related dataset comprising at least one new temporal event sequence; and
evaluating the at least one new temporal event sequence using the decision boundary to label the at least one new temporal event sequence as an abnormal sequence, causing a computer system to issue an alert,wherein learning the classifier learning comprises;
randomly initializing a solution space (Ω
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constructing an undirected graph for the normal temporal event sequences in the first computer process related dataset;
capturing at least one temporal dynamic of the normal temporal event sequences of the first computer process related dataset;
assigning labels to each of the normal temporal event sequences of the first computer process related dataset by computing, for each of the normal temporal event sequences a probability that the at least one normal temporal event sequence of the first computer process related dataset is a normal sequence or an abnormal sequence, wherein at least one of the at least one normal temporal event sequences is labeled as an abnormal sequence; and
refining the classifier using the plurality of normal temporal event sequences of the first computer process related dataset with respective labels.
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Abstract
A dataset including at least one temporal event sequence is collected. A one-class sequence classifier f(x) that obtains a decision boundary is statistically learned. At least one new temporal event sequence is evaluated, wherein the at least one new temporal event sequence is outside of the dataset. It is determined whether the at least one new temporal event sequence is one of a normal sequence or an abnormal sequence based on the evaluation. Numerous additional aspects are disclosed.
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Citations
19 Claims
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1. A method comprising:
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collecting a first computer process related dataset comprising a plurality of normal temporal event sequences; learning, using the first computer process related dataset, a one-class sequence classifier f(x) that obtains a decision boundary configured to label temporal event sequences; collecting a second computer process related dataset comprising at least one new temporal event sequence; and evaluating the at least one new temporal event sequence using the decision boundary to label the at least one new temporal event sequence as an abnormal sequence, causing a computer system to issue an alert, wherein learning the classifier learning comprises; randomly initializing a solution space (Ω
);constructing an undirected graph for the normal temporal event sequences in the first computer process related dataset; capturing at least one temporal dynamic of the normal temporal event sequences of the first computer process related dataset; assigning labels to each of the normal temporal event sequences of the first computer process related dataset by computing, for each of the normal temporal event sequences a probability that the at least one normal temporal event sequence of the first computer process related dataset is a normal sequence or an abnormal sequence, wherein at least one of the at least one normal temporal event sequences is labeled as an abnormal sequence; and refining the classifier using the plurality of normal temporal event sequences of the first computer process related dataset with respective labels. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer program product comprising a non-transitory computer readable storage medium having computer readable program code embodied therewith, said computer readable program code comprising:
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computer readable program code configured to; collect a dataset comprising a plurality of normal temporal event sequences; learn, using the dataset, a one-class sequence classifier f(x) that obtains a decision boundary labeling each temporal event sequence; evaluate, using the decision boundary, at least one new temporal event sequence, wherein the at least one new temporal event sequence is outside of the dataset; and determine that the at least one new temporal event sequence is an abnormal sequence based on the evaluating step, causing an alert to be issued, wherein the computer readable program code configured to learn the classifier learning comprises computer readable program code configured to; randomly initialize a solution space (Ω
);construct an undirected graph for the normal temporal event sequences in the dataset; capture at least one temporal dynamic of the normal temporal event sequences of the dataset; assign labels to each of the normal temporal event sequences of the dataset by computing, for each of the normal temporal event sequences a probability that the at least one normal temporal event sequence of the dataset is a normal sequence or an abnormal sequence, wherein at least one of the at least one normal temporal event sequences is labeled as an abnormal sequence; and refine the classifier using the plurality of normal temporal event sequences of the dataset with respective labels.
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13. An apparatus comprising:
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a memory; and at least one processor, coupled to said memory, and operative to; collect a dataset comprising a plurality of normal temporal event sequences; learn, using the plurality of normal temporal event sequences, a one-class sequence classifier f(x) that obtains a decision boundary labeling each temporal event sequence; evaluate, using the decision boundary, at least one new temporal event sequence, wherein the at least one new temporal event sequence is outside of the dataset; and determine that the at least one new temporal event sequence is an abnormal sequence based on the evaluating step, causing an alert to be issued, wherein the processor in learning the one-class sequence classifier f(x) is operative to; randomly initialize a solution space (Ω
);construct an undirected graph for the normal temporal event sequences in the dataset; capture at least one temporal dynamic of the normal temporal event sequences of the dataset; assign labels to each of the normal temporal event sequences of the dataset by computing, for each of the normal temporal event sequences a probability that the at least one normal temporal event sequence of the dataset is a normal sequence or an abnormal sequence, wherein at least one of the at least one normal temporal event sequences is labeled as an abnormal sequence; and refine the classifier using the plurality of normal temporal event sequences of the dataset with respective labels. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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Specification