Anomaly Detection for Discrete Sequences: A Survey

By Varun Chandola, Arindam Banerjee, Vipin Kumar. IEEE Transactions on Knowledge and Data Engineering, 2012

633 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/53df899e1a18082b8654677b4c0aa95dbd96f0e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Biomedical Engineering

In Computer Networks and Communications

Citations in the corpus

Anomaly detection in dynamic networks: a survey (2015, methodology): “Vertex detection [55, 56, 67, 106] Subgraph detection [3, 13, 30, 31] Change detection [38] Edge detection [22] Change detection [112] Vertex detection [129] Event detection [68, 72, 75, 77, 83, 84, 93, 113, 114, 115, 122] Change detection [7, 65, 66] Edge detection [1, 80] Subgraph detection [60, 8”
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series (2018, background): “dentify whether the testing data conform to the normal data distribution; the non-conforming points are called anomalies, outliers, intrusions, failures or contaminants in various application domains [3], [2]. Anomaly detection is an old but challenging problem—it has been studied in the statistics ”
Smart Anomaly Detection in Sensor Systems (2020, background): “Techniques for discrete sequences have been introduced in [13].”
Outlier Analysis (2013, methodology): “The transformation to the discrete case also enables the use of supervised string-based models such as Hidden Markov Models (HMM) [108].”
Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013, background): “Detected unusual patterns in health parameters, especially for home monitoring systems, enables the clinicians to make accurate decisions in short time [25].”
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams (2017, background): “A comprehensive overview of anomaly detection provided by Chandola et al. (2012) concludes that anomaly detection techniques for online and multivariate sequences are underdeveloped; both issues are addressed in this paper.”
A least-squares approach to anomaly detection in static and sequential data (2014, background): “Anomaly detection is often applied in a sequential setting, where we have temporal information to aid the inference process (Chandola et al., 2012; Quinn and Williams, 2007; Song et al., 2013; Tan et al., 2011).”
Machine Learning for Anomaly Detection: A Systematic Review (2021, background): “The same authors introduced another survey [10] on the topic of anomaly detection for discrete sequence.”
Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014, background): “Some popular models are based on the distance between objects [37; 60; 4; 74], or on the density of the neighborhood of an object [9; 56; 34; 38; 42], or based on the variance of angles between object vectors [42; 58], or on other principles of outlierness in various domains [12; 13; 3].”
Anomaly detection in online social networks (2014, neutral): “A number of methods for detecting anomalies within discrete sequences are discussed in (Chandola et al., 2012), and we believe that many of these methods could be applied to the detection of dynamic labelled anomalies in social networks.”
A Unifying Review of Deep and Shallow Anomaly Detection (2020, background): “These include point anomalies, conditional or contextual anomalies [169], [171], [191]–[195], and group or collective anomalies [146], [193], [196]–[199].”
Graph based anomaly detection and description: a survey (2014, methodology): “In addition, survey and special issue journal articles that address anomaly, event, and change detection include [Chandola et al., 2012, Margineantu et al., 2010, Radke et al., 2005].”
Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, background): “The different anomaly types, pattern models, and time series properties led to the development of amultitude of different anomaly detection algorithms, about which various surveys exist [12, 20, 27, 28, 29, 37, 38, 53, 63].”