Fast online anomaly detection using scan statistics

Abstract

We present methods to do fast online anomaly detection using scan statistics. Scan statistics have long been used to detect statistically significant bursts of events. We extend the scan statistics framework to handle many practical issues that occur in application: dealing with an unknown background rate of events, allowing for slow natural changes in background frequency, the inverse problem of finding an unusual lack of events, and setting the test parameters to maximize power. We demonstrate its use on real and synthetic data sets with comparison to other methods.

Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects

“…It detects statistically substantial bursts of events. In the field of Seismology, anomalous events are detected [41] by applying online scan statistics anomaly detection technique to identify unusual bursts of events in the earthquake database.…”

“…In any application domain, the main aim is to detect all of the anomalies which are ideal in a real case. The unsupervised clustering algorithms specifically K-means and DBSCAN [41] as a reasonable way to deal with identify abnormalities on various dimensionality and cluster overlap yet the false negatives are shaped when the calculation neglects to order as peculiarities and considers as an ordinary point. The advantage is restricted from a social point of view as we have probed a recreated informational collection in a controlled climate without considering the elements engaged with certifiable situations such as noise.…”

“…The graph-based anomaly is detected where the author paper graph of this project contains several authors, papers and edges. Likewise, the researcher used the database (10,344 articles and 10,579 authors) from the COM2 [55] Tensor decomposition Tensor decomposition over time Bayesian approach [7] p values Predictive p value Scan statistics [41] Scan statistics Scan stat. and vertex scores Dynamic influence network (DIN-Viz) [66] Community memberships Influence of rule-based models Periodic region detection (PRED) [73] Community memberships periodic mobility patterns Grid-based subspace [80] Graphical scores Locality for HD cases Netspot [69] Scores of regions Scores of regions gSkeleton-Clu [62] Community memberships Community structure GLAD model [18] Community memberships Pair and point-wise data ASCOS [46] Community memberships Similarity scores MultiAspect-forensics [52] Scores of regions Pattern discovery in a heterogeneous network P-Rank [9] p values Predictive p value Relational neighbor (RN) [20] Graphical scores Class labels of related neighbors CBLOF [4] Community memberships Anomalous community detection xStream [24] Graphical scores Anomalies for feature evolving streams FIRST [11] Graphical scores Attributed subgraph matching Streaming-POT [26] Scores of regions Extreme value-theory One-class support measure machine (OCSMM) [1] Scores of regions Anomalous behavior of data points Temporal clustering (TC) [40] Community memberships Causal events Conditional and marginal AD tree [43] Graphical scores Point anomalous records sixteenth library and information science (LIS) conference [86] to detect sub-groups anomalies from the largest cluster and finds edge betweenness for all edges.…”

From Reflex to Reflection: Two Tricks AI Could Learn from Us

“…Statistical devices can detect anomalies, but only when they know in advance which dimensions are relevant to monitor. Any instance that is several standard deviations away from the mean along those dimensions is then considered as an anomaly [21]. The problem is that in high-dimensional spaces, one does not always know which dimensions are relevant.…”

Variable Window Scan Statistics for Poisson Processes

“…We first review the derivation of the expected TTD according to Turner et al (2010). This formula is not accurate for large window sizes and we will later derive an improved formula.…”