A Comparative Evaluation Of Semi-supervised Anomaly Detection Techniques

Detta är en Kandidat-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS)

Författare: Rebwar Bajallan; Burhan Hashi; [2020]

Nyckelord: ;

Sammanfattning: As we are entering the information age and the amount of data is rapidly increasing, the task of detecting anomalies has become a necessity in many organizations as anomalies often reveal useful information which in many cases can be critical to save lives or to catch imposters. The semi-supervised approach to anomaly detection which is based on the fact that the user has no information about anomalies has become widely popular since it’s easier to model the normal state of systems than to obtain information about every anomalous behavior. Therefore, in this study we choose to conduct a comparative evaluation of the semi-supervised anomaly detection techniques; Autoencoder, Local outlier factor algorithm, and one class support vector machine, to simplify the process of selecting the right technique when faced with similar anomaly detection problems of semi-supervised nature. We found that the local outlier factor algorithm was superior in performance given the Electrocardiograms dataset (ECG5000), achieving a high precision and perfect recall. The autoencoder achieved the best performance given the credit card fraud dataset, even though the remaining models also achieved a relatively high performance that didn’t differ much from that of the autoencoder. However, it should be noted that the definition of performance differs as the characteristics of anomaly detection problems are different, as specific problems might put a higher weight on detecting

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