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Collective anomalies co-occurrence of data instances in a particular…
Collective anomalies
co-occurrence of data instances
in a particular form that makes them anomalies
Requirement
Relationship between data instances
Sequential anomalies
Symbolic
Biological
time-series, continuous
Univariate/Multivariate
Spatial anomalies
Graph anomalies
Spatial anomalies
Finding subgraphs or subcomponents in the data that are anomalous.
Graph anomalies
Detecting anomalous subgraphs in a given large graph
measure the regularity or entropy of the sub-graph in the context of the entire graph to determine its anomaly score
analyze the frequency of a subgraph in the given graph to determine if it is an anomaly or not.
Sequential anomalies
Detecting anomalous sequence
Sequences might not be of equal length
Sequences may not be aligned with each other
Supervised
Semi-supervised
Are sequences aligned ?
Yes
Transform the sequences to a finite feature space and then use a point anomaly detection technique
in the new space to detect anomalies
No
Semi-supervised
time-based inductive learning
generate rules from the set of normal sequences
Finite State Automatons (FSA)
Markov models
Hidden Markov Model
Probabilistic Suffix Trees (PST)
Sparse Markov Trees (SMT)
Does normal behavior of the time-series follows a defined pattern ?
Yes
Unsupervised
Window Comparison Anomaly
Detection (WCAD)
HOT SAX
Maximum Entropy Markov Models (Maxent)
Conditional Random Fields (CRF)
length of the anomalous subsequence to be detected is not generally de¯ned
challenging to
create a robust model of normalcy