Change and Anomaly Detection in Data Streams

Tutorial “ Change and Anomaly Detection in Signals, Images, and General Data Streams ”

Presenters : Giacomo Boracchi, Politecnico di Milano, DEIB; Diego Carrera, System Research and Applications, STMicroelectronics, Agrate Brianza

Motivation
Change and anomaly detection problems are ubiquitous in science and engineering. The prompt detection of changes or anomalous patterns is often a primary concern, as they provide precious information for understanding the dynamics of a monitored process and for activating suitable countermeasures. Changes, for instance, might indicate an unforeseen evolution of the process generating the data, or a fault in a machinery. Anomalies are typically the most informative regions in an image (e.g., defects in images used for quality control) or the most relevant patterns in a time series (e.g., arrhythmias in ECG tracing) or data stream (e.g. frauds in credit card transactions). Not surprisingly, detection problems in datastreams / time series / images have been widely investigated in the image analysis and pattern recognition communities and are key in application scenarios ranging from quality inspection to health monitoring.

Aims and scope
The tutorial presents a rigorous formulation of the change and anomaly-detection problems that fits many signal/image analysis techniques and applications. The tutorial describes in detail the most important approaches in the literature, following the machine-learning perspective of supervised, semi-supervised and unsupervised monitoring tasks. Special emphasis will be given to detection methods based on learned models, which are often adopted to handle images and signals. In particular, these will be divided into traditional models (including autoencoders, learned projections and dictionaries yielding sparse representations) and deep learning models (including CNNs, deep-one-class classifiers and deep generative models)
The tutorial is accompanied by various examples where change/anomaly detection algorithms are applied to solve real world problems. These include health applications (ECG monitoring in wearable devices), quality control (image analysis solutions to detect defects and anomalous patterns in industrial manufacturing), and fraud detection in a datastream of credit card transactions.

Slides

ICPR 2020 Tutorial: Change and Anomaly Detection in Images, Signals and datastreams
Giacomo Boracchi and Diego Carrera Tutorial at IEEE ICPR 2020;
January 21 2021, Milano
(Slides);

References

[Frittoli et al. 2020] Strengthening Sequential Side-Channel Attacks Through Change Detection
Luca Frittoli, Matteo Bocchi, Silvia Mella, Diego Carrera, Beatrice Rossi, Pasqualina Fragneto, Ruggero Susella and Giacomo Boracchi , IACR Transactions on Cryptographic Hardware and Embedded Systems (TCHES), 2020(3), pp. 1-21 doi: 10.13154/tches.v2020.i3.1-21
(Original Open Access), (Video Presentation)

[Leveni et al.] Anomaly detection via preference embedding
Filippo Leveni, Luca Magri, Giacomo Boracchi, Cesare Alippi , International Conference on Pattern Recognition (ICPR) 2020,
(A href="../docs/2020_ICPR_PIF Anomaly detection via preference embedding_Leveni.pdf" target="_blank">Preprint)

[Carrera et al. 2019] Online Anomaly Detection for Long-Term ECG Monitoring using Wearable Devices
Diego Carrera, Beatrice Rossi, Pasqualina Fragneto, Giacomo Boracchi , Pattern Recognition Volume 88, April 2019, Pages 482-492 doi:10.1016/j.patcog.2018.11.019
(Preprint), (Demo Page)

[di Bella et al. 2019] Wafer Defect Map Classification Using Sparse Convolutional Networks
Roberto di Bella, Diego Carrera, Beatrice Rossi, Pasqualina Fragneto, Giacomo Boracchi , International Conference on Image Analysis and Processing, 125-136 (ICIAP) 2019, Trento doi:10.1007/978-3-030-30645-8_12
(Preprint),

[Boracchi et al. 2018] QuantTree: Histograms for Change Detection in Multivariate Data Streams
Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Maccio' , International Conference on Machine Learning (ICML) 2018
(Paper), (Source Code), (Bibtex).

[Alippi et al. 2016] Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss
Cesare Alippi, Giacomo Boracchi, Diego Carrera, Manuel Roveri , International Joint Conference of Artificial Intelligence (IJCAI) 2016, New York, USA, July 9 - 13
(Preprint), (Original), (BibTeX).

[Carrera et al. 2016 a] ECG Monitoring in Wearable Devices by Sparse Models
Diego Carrera, Beatrice Rossi, Daniele Zambon, Pasqualina Fragneto, and Giacomo Boracchi , Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, ECML-PKDD 2016, Riva del Garda, Italy, September 19 - 23, Accepted, 16 pages
(Preprint)

[Carrera et al. 2016 b] Defect Detection in SEM Images of Nanofibrous Materials
Diego Carrera, Fabio Manganini, Giacomo Boracchi, Ettore Lanzarone , IEEE Transactions on Industrial Informatics -- In Press, 11 pages, doi:10.1109/TII.2016.2641472
(Preprint), (Original), (Dataset),

[Carrera et al. 2016 c] CCM: Controlling the Change Magnitude in High Dimensional Data
Cesare Alippi, Giacomo Boracchi, Diego Carrera , Proceedings of INNS Conference on Big Data, 2016 , Thessaloniki, Greece, October 23 - 25, 2016, 10 pages
(Preprint), (Slides) (Codes).
This paper has received the Best Regular Paper Award.

[Dal Pozzolo et al. 2015] Credit Card Fraud Detection and Concept-Drift Adaptation with Delayed Supervised Information
Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, Cesare Alippi and Gianluca Bontempi , Proceedings of International Joint Conference on Neural Networks IJCNN 2015, Killarney, Irland, July 12 - 17,
(Preprint), (BibTeX), (Original).

[Boracchi and Roveri. 2014] Exploiting Self-Similarity for Change Detection
Giacomo Boracchi, Manuel Roveri IJCNN 2014 International Joint Conference on Neural Networks, Beijing, China July 6 - 11,
(Preprint)

[Alippi et al. 2011 a] A Hierarchical, Nonparametric Sequential Change-Detection Test
Cesare Alippi, Giacomo Boracchi and Manuel Roveri, in Proceedings of IJCNN 2011, the International Joint Conference on Neural Networks, San Jose, California July 31 - August 5, 2011. pp 2889 - 2896, doi: 10.1109/IJCNN.2011.6033600
(Preprint), (BibTeX), (Original)

[Alippi et al. 2011 b] A just-in-time adaptive classification system based on the intersection of confidence intervals rule,
Cesare Alippi, Giacomo Boracchi, Manuel Roveri Neural Networks, Elsevier vol. 24 (2011), pp. 791-800
doi: 10.1016/j.neunet.2011.05.012
(Preprint), (BibTeX), (Original)

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