Hybridization of Adaptive Cuckoo's Search Algorithm with Core Vector Machine for Feature Selection
DOI:
https://doi.org/10.17762/msea.v71i4.1070Abstract
As an important catalyst in a fast digitizing world, Cloud computing offers great opportunities in creating scalability in resource sharing to perform transparent computation, while allowing seamless transfer of information as well. Researchers face an uphill task to ensure that data, information sources, software and cloud materials remain safe and secure. Cloud security measures integrated into the cloud computing ecosystem help in securing the cloud resources.In such a background, Anomaly Detection Systems (ADS) offer us the best possibilities in building detection control mechanisms. However, when network traffic data is efficiently managed with the application of a machine learning algorithm, it results in building the accuracy capability of the ADS. In this paper, AdaptiveCuckoo's Search Algorithm is hybridized within a CoreVector Machine to ensure better feature selection results.