Anomaly Detection System for Network Traffic using Data Mining - Sharma, Ruby;Chaurasia, Sandeep
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Anomaly detection using Density Maximization Fuzzy C-means Algorithm: The rationale for the anomaly detection system using density maximization approach to the fuzzy c-means clustering algorithm. The workflow of a proposed anomaly detection system with density maximization FCM algorithm. The framework of ensemble classifier-based anomaly detection - this approach of anomalous detection is based on the integration of multiple classifiers so that the weakness of one classifier can be compensated by the other classifier. The workflow of the proposed intrusion detection framework based on an ensemble classifier.…mehr

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Produktbeschreibung
Anomaly detection using Density Maximization Fuzzy C-means Algorithm: The rationale for the anomaly detection system using density maximization approach to the fuzzy c-means clustering algorithm. The workflow of a proposed anomaly detection system with density maximization FCM algorithm. The framework of ensemble classifier-based anomaly detection - this approach of anomalous detection is based on the integration of multiple classifiers so that the weakness of one classifier can be compensated by the other classifier. The workflow of the proposed intrusion detection framework based on an ensemble classifier.
Autorenporträt
Mrs. Ruby Sharma has completed her B.Sc.(Agri) in 2011 and M.Sc.(Agri) in 2013 from Assam Agricultural University, Jorhat, Assam. She has been pursuing her Ph.D. from the same institute in the Department of Horticulture on Hydroponic Floriculture. She has been awarded with INSPIRE fellowship from DST, Govt. of India in 2014.