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