Anomaly Detection With Multivariate K-sigma Score Using Monte Carlo
| dc.contributor.author | Cetin, Uzay | |
| dc.contributor.author | Tasgin, Mursel | |
| dc.date.accessioned | 2024-07-18T20:47:28Z | |
| dc.date.available | 2024-07-18T20:47:28Z | |
| dc.date.issued | 2020 | |
| dc.department | İstanbul Bilgi Üniversitesi | en_US |
| dc.description | 5th International Conference on Computer Science and Engineering (UBMK) -- SEP 09-11, 2020 -- Diyarbakir, TURKEY | en_US |
| dc.description.abstract | We propose a new unsupervised anomaly detection technique based on Monte Carlo sampling. We draw samples from a Gaussian probability distribution that fits to the given data and use these samples to form a Monte Carlo estimate of an anomaly score. Many of the current anomaly detection algorithms detect anomalies without producing any anomaly scores. This may lead to confusion over the scaling of the anomaly situation as well as identifying the underlying cause for anomalies in explainable conditions. The proposed algorithm is a multi-variate unsupervised technique, which also provides anomaly scores that allows measuring the strength of outlierness of a multi-variate data point. Our experiments show that the proposed algorithm achieves better results compared to the state-of-the-art anomaly detection algorithms with clustering-based approaches and isolation forest. | en_US |
| dc.description.sponsorship | IEEE Turkey Sect,Istanbul Teknik Univ,Gazi Univ,Atilim Univ,Dicle Univ,Turkiye Bilisim Vakfi,Kocaeli Univ | en_US |
| dc.description.sponsorship | Tubitak Teydeb program | en_US |
| dc.description.sponsorship | This work is accomplished as a research project under the research and development (R&D) department at KKB Kredi Kayit Burosu, as a part of the efforts in MPP-62 Proactive Unsupervised Anomaly Detection on System Data project that is supported by Tubitak Teydeb program. We thank our dedicated R&D team for their invaluable contributions to this work. | en_US |
| dc.identifier.doi | 10.1109/ubmk50275.2020.9219482 | |
| dc.identifier.endpage | 98 | en_US |
| dc.identifier.isbn | 978-1-7281-7565-2 | |
| dc.identifier.scopus | 2-s2.0-85095717525 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.startpage | 94 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/ubmk50275.2020.9219482 | |
| dc.identifier.uri | https://hdl.handle.net/11411/7803 | |
| dc.identifier.wos | WOS:000629055500018 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 2020 5th International Conference on Computer Science and Engineering (Ubmk) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Anomaly Detection | en_US |
| dc.subject | Clustering | en_US |
| dc.subject | Monte Carlo Sampling | en_US |
| dc.subject | Isolation Forest | en_US |
| dc.subject | Machine Learning | en_US |
| dc.title | Anomaly Detection With Multivariate K-sigma Score Using Monte Carlo | |
| dc.type | Conference Object |
