Cuckoo Search Algorithm and BF Tree Used for Anomaly Detection in Data Mining
||International Journal of Computer & Organization Trends (IJCOT)||
|© 2019 by IJCOT Journal|
|Volume - 9 Issue - 4
|Year of Publication : 2019|
|Authors : Shubhi Kulshrestha, Ankur Goyal|
|DOI : 10.14445/22492593/IJCOT-V9I4P303|
MLA Style:Shubhi Kulshrestha, Ankur Goyal "Cuckoo Search Algorithm and BF Tree Used for Anomaly Detection in Data Mining" International Journal of Computer and Organization Trends 9.4 (2019): 11-18.
APA Style:Shubhi Kulshrestha, Ankur Goyal (2019). Cuckoo Search Algorithm and BF Tree Used for Anomaly Detection in Data Mining International Journal of Computer and Organization Trends, 9(4), 11-18.
Anomaly detection is the new research topic of this new generation of researcher’s today. Defect detection is a domain, that is, the key towards impending data mining. The term mining model refers towards methods & algo’s that allow data towards be extracted & analyzed so that the rules & patterns that characterize the data can be found. Towards learn more about hidden structures & connections, the technology of data mining(DM)may be practical towards any type of data. In the current world, a lot of data is transported from one place towards another. Data that is transmitted or stored is subject towards attack. Although there are several techniques or applications available towards secure data, there are still ambiguities. It helps towards analyze resulting data & determine several types of attack data mining techniques that are open towards occurrence. Anomaly detection is technique of DMto detect astonishing or unanticipated behavior concealed in data that increases probabilities of penetrating or attacking. This learning proposes a model aimed at structure a NW penetration detection system by a machine learning (ML) algo & the system is primarily a fault-based penetration detection. In this paper, the feature selection Cuckoo search algo is performed, & the classification is mainly performed by the BF tree. It shows that the proposed work produces better results & the detection error attributes are more accurate.
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Data mining, Intrusion Detection System, Anomaly IDS, K Means, SMO, Genetic Algorithm, Cuckoo Search Algorithm and BF tree.