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Conformal Prediction for Distribution-Independent Anomaly Detection in Streaming Vessel Data
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (SAIL)
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (SAIL)
2010 (Engelska)Ingår i: StreamKDD '10: Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques, Association for Computing Machinery (ACM), 2010, s. 47-55Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper presents a novel application of the theory of conformal prediction for distribution-independent on-line learning and anomaly detection. We exploit the fact that conformal predictors give valid prediction sets at specified confidence levels under the relatively weak assumption that the (normal) training data together with (normal) observations to be predicted have been generated from the same distribution. If the actual observation is not included in the possibly empty prediction set, it is classified as anomalous at the corresponding significance level. Interpreting the significance level as an upper bound of the probability that a normal observation is mistakenly classified as anomalous, we can conveniently adjust the sensitivity to anomalies while controlling the rate of false alarms without having to find any application specific thresholds. The proposed method has been evaluated in the domain of sea surveillance using recorded data assumed to be normal. The validity of the prediction sets is justified by the empirical error rate which is just below the significance level. In addition, experiments with simulated anomalous data indicate that anomaly detection sensitivity is superior to that of two previously proposed methods.

Ort, förlag, år, upplaga, sidor
Association for Computing Machinery (ACM), 2010. s. 47-55
Nyckelord [en]
Anomaly detection, conformal prediction, sea surveillance
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Teknik
Identifikatorer
URN: urn:nbn:se:his:diva-4656DOI: 10.1145/1833280.1833287Scopus ID: 2-s2.0-77956247958ISBN: 978-1-4503-0226-5 OAI: oai:DiVA.org:his-4656DiVA, id: diva2:392598
Konferens
1st International Workshop on Novel Data Stream Pattern Mining Techniques, StreamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining; Washington, DC; 25 July 2010 through 25 July 2010
Tillgänglig från: 2011-01-27 Skapad: 2011-01-27 Senast uppdaterad: 2018-01-12Bibliografiskt granskad

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Laxhammar, RikardFalkman, Göran

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