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UID:692@lincs.fr
DTSTART;TZID=Europe/Paris:20220218T143000
DTEND;TZID=Europe/Paris:20220218T170000
DTSTAMP:20220217T121502Z
URL:https://www.lincs.fr/events/phd-thesis-defense-andrian-putina/
SUMMARY:PhD thesis defense "Unsupervised Anomaly Detection: Methods and
 applications"
DESCRIPTION:An anomaly (also known as outlier) is an instance that
 significantly deviates from the rest of the input data and being defined by
 Hawkins as an observation\, which deviates so much from other observations
 as to arouse suspicions that it was generated by a different
 mechanism.Anomaly detection (also known as outlier or novelty detection) is
 thus the machine learning and data mining field with the purpose of
 identifying those instances whose features appear to be inconsistent with
 the remainder of the dataset. In many applications\, correctly
 distinguishing the set of anomalous data points (outliers) from the set of
 normal ones (inliers) proves to be very important. A first application is
 data cleaning\, i.e.\, identifying noisy and fallacious measurement in a
 dataset before further applying learning algorithms.However\, with the
 explosive growth of data volume collectable from various sources\, e.g.\,
 card transactions\, internet connections\, temperature measurements\, etc.
 the use of anomaly detection becomes a crucial stand-alone task for
 continuous monitoring of the systems. In this context\, anomaly detection
 can be used to detect ongoing intrusion attacks\, faulty sensor networks or
 cancerous masses.The thesis proposes first a batch tree-based approach for
 unsupervised anomaly detection\, called Random Histogram Forest (RHF). The
 algorithm solves the curse of dimensionality problem using the fourth
 central moment (aka kurtosis) in the model construction while boasting
 linear running time. A stream based anomaly detection engine\, called ODS\,
 that leverages DenStream\, an unsupervised clustering technique is
 presented subsequently and finally Automated Anomaly Detection engine which
 alleviates the human effort required when dealing with several algorithm
 and hyper-parameters is presented as last contribution.
CATEGORIES:PhD Defense
LOCATION:Zoom + Amphi 5 chez Télécom-Paris\, 19 place Marguerite Perey\,
 Palaiseau\, 91120\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 place Marguerite Perey\,
 Palaiseau\, 91120\, France;X-APPLE-RADIUS=100;X-TITLE=Zoom + Amphi 5 chez
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DTSTART:20211031T020000
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