Hybridization of Adaptive Cuckoo's Search Algorithm with Core Vector Machine for Feature Selection

Authors

  • Srinivasa Rao Pokuri, Dr. Nagaraju Devarakonda

DOI:

https://doi.org/10.17762/msea.v71i4.1070

Abstract

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.

Downloads

Published

2022-10-13

How to Cite

Srinivasa Rao Pokuri, Dr. Nagaraju Devarakonda. (2022). Hybridization of Adaptive Cuckoo’s Search Algorithm with Core Vector Machine for Feature Selection. Mathematical Statistician and Engineering Applications, 71(4), 4740–4748. https://doi.org/10.17762/msea.v71i4.1070

Issue

Section

Articles