DocumentCode :
3111872
Title :
Finding Outlier from Large Dataset Using Online OSPCA
Author :
Patil, Priyanka R. ; Manekar, Amitkumar S.
Author_Institution :
Comput. Dept., Sandip Inst. of Technol. & Res. Centre. Nasik, Nasik, India
fYear :
2015
fDate :
26-27 Feb. 2015
Firstpage :
379
Lastpage :
381
Abstract :
Anomaly Detection is the term which is widely used in Data Mining. Anomaly Detection means Fraud Detection. Anomalous Intrusion became a key issue in security because of the heavy data in network. So it becomes hard to prevent such attacks. Previous techniques works only on batch mode means those techniques are not applied for large dataset. For this purpose it is important to find technique which provides support for large dataset. The HMM and OSPCA are the techniques which are applied for large dataset by using online updating technique. These Techniques are used in the applications such as Fraud Detections Systems like Intrusion Detection Technique.
Keywords :
data mining; hidden Markov models; principal component analysis; security of data; HMM; anomalous intrusion; anomaly detection; batch mode; data mining; fraud detection systems; hidden Markov model; intrusion detection technique; online OSPCA; online updating technique; outlier detection method; Computers; Covariance matrices; Data mining; Hidden Markov models; Memory management; Principal component analysis; Training; Anomaly detection; HMM; Oversampling; online updating;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on
Conference_Location :
Pune
Type :
conf
DOI :
10.1109/ICCUBEA.2015.79
Filename :
7155872
Link To Document :
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